# Situation WorkDash has aggregated a comprehensive, synthetic week of operational data, including email, Teams messages, server logs, and calendar notes, to evaluate the readiness of its internal benchmarking infrastructure. The primary focus of this week's activity is the evaluation of a specific large language model—Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf—running on an AMD R9700 GPU. The benchmarking effort is designed to measure model performance across multiple dimensions, including throughput, reliability, latency, MTP (Mixture of Token Probabilities) acceptance, and output quality. However, the week has revealed several critical technical bottlenecks, privacy concerns, and design requirements that must be addressed before the benchmarking suite can be considered production-ready. On the technical side, the system is experiencing significant performance degradation when evaluating prompts with a 262144 token context using llama.cpp, and the available VRAM on the R9700 is nearly exhausted by the model load, leaving minimal headroom for context caching. Furthermore, user feedback indicates that the current benchmark tasks are overly simplistic ("toy tests") and fail to represent real-world usage, which requires long final outputs and complex reasoning. On the privacy and operational side, WorkDash's architecture, which processes internal email and Teams content, introduces significant data handling risks. Strict protocols have been established to ensure that raw messages are stored as local artifacts only, with redacted previews stored in SQLite, and that private source data never leaves the lab. Finally, the dashboard and reporting infrastructure requires substantial development to capture the necessary metrics, visualize the data effectively, and prepare a publishable public writeup that describes aggregate model behavior without exposing private information. This report provides a comprehensive incident-style analysis and benchmark-readiness assessment based strictly on the provided source packets. It details the evidence timeline, technical and privacy findings, benchmark design requirements, reporting requirements, risks, and recommended next actions, culminating in a publishable summary suitable for external distribution. # Evidence Timeline The following timeline chronologically details the events, observations, and directives captured by WorkDash over the course of the week. **Monday** * **08:14:** Alex reports a critical performance issue: llama.cpp prompt evaluation is slow after enabling a 262144 token context. This indicates a severe bottleneck in the inference engine when handling very long contexts, likely related to memory bandwidth or compute limitations. * **09:02:** Priya provides hardware context, noting that the R9700 has 32624 MiB of VRAM. However, the model load consumes approximately 31016 MiB after warmup. This leaves only about 1608 MiB of VRAM for KV cache and other overhead, which is critically insufficient for a 262144 token context, explaining the slow prompt evaluation. * **10:30:** A Teams message outlines the requirements for the benchmark dashboard. It must include screenshots showing throughput, reliability, latency, MTP acceptance, and output quality. This establishes the core metrics that the dashboard must visualize. * **13:42:** Server logs reveal that for the current MTP profile, the ratio of draft_n_accepted / draft_n is usually between 0.75 and 0.90. This indicates a high acceptance rate for MTP draft tokens, suggesting that the MTP mechanism is functioning effectively and providing a significant throughput boost. **Tuesday** * **07:55:** A project note emphasizes that benchmark outputs must stay private because WorkDash may process email and Teams content. This highlights the primary privacy constraint for the entire benchmarking pipeline. * **11:12:** A model note confirms that Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf is active and running with a --reasoning-budget of 8192. This defines the specific model and its configuration for the current benchmarking cycle. * **14:40:** A user complaint states that toy tests are not representative; real tasks should produce long final outputs and include reasoning. This feedback directly challenges the validity of the current benchmark tasks and necessitates a redesign of the test suite. **Wednesday** * **09:25:** A maintenance task specifies the exact data points that must be captured for every benchmark run: model path, quant, context, backend, MTP settings, reasoning budget, prompt tokens, completion tokens, duration, and GPU memory. This establishes the comprehensive telemetry requirements for the benchmarking system. * **10:01:** A privacy note dictates the data storage architecture: raw messages should be stored as local artifacts only, with redacted previews stored in SQLite. This reinforces the privacy constraints and defines the technical implementation for data isolation. * **15:16:** A dashboard note expands on the visualization requirements, asking for a model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison. This provides a detailed blueprint for the dashboard's analytical capabilities. **Thursday** * **08:08:** A support note specifies that OpenWebUI, AgentSSH, Cline, opencode, and WorkDash should be compared as traffic types, but the system should not require app-specific integrations. This defines the scope of the traffic comparison and the architectural constraint of avoiding tight coupling with specific applications. * **12:34:** A reliability note states that each benchmark task should stop after failure unless --keep-going is supplied. This establishes the default failure handling behavior for the benchmark runner. **Friday** * **09:00:** A planning note clarifies the public reporting strategy: the first publishable public writeup can describe aggregate model behavior, but the private source data must not leave the lab. This sets the boundary for what can be shared externally. * **16:50:** A server note warns that long outputs can run for many minutes, so progress, artifacts, and partial failure reporting matter. This highlights a critical operational requirement for handling long-running tasks, ensuring that users and systems are not left in a state of uncertainty. # Technical Findings The technical analysis of the week's data reveals several critical findings regarding hardware constraints, model performance, MTP behavior, and system architecture. **Hardware and VRAM Constraints** The R9700 GPU provides 32624 MiB of VRAM [E2]. The active model, Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf, consumes approximately 31016 MiB of VRAM after warmup [E2]. This leaves a mere 1608 MiB of VRAM for KV cache, prompt processing, and other overhead. When Alex reports that llama.cpp prompt evaluation is slow after enabling a 262144 token context [E1], it is directly attributable to this VRAM constraint. A 262144 token context requires a massive KV cache, which cannot fit in the remaining 1608 MiB. Consequently, the system is likely forced to offload KV cache to system RAM or swap, resulting in severe performance degradation. This finding dictates that any benchmark involving long contexts must either use a smaller context window, a model with a smaller footprint, or a GPU with significantly more VRAM. **Model and MTP Performance** The active model is Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf, configured with a --reasoning-budget of 8192 [E6]. The server logs indicate that the MTP profile is performing well, with a draft_n_accepted / draft_n ratio of 0.75 to 0.90 [E4]. This high acceptance rate means that 75% to 90% of the draft tokens generated by the MTP mechanism are accepted by the main model, significantly accelerating throughput. However, the slow prompt evaluation at 262144 tokens [E1] suggests that the MTP acceleration is being bottlenecked by the context window size, not by the MTP mechanism itself. The reasoning budget of 8192 [E6] is also a critical parameter, as it limits the depth of the model's reasoning process, which may impact the quality of long outputs. **Context Window and Prompt Evaluation** The 262144 token context is a significant stress test for the system. The slow prompt evaluation [E1] indicates that the current llama.cpp configuration is not optimized for such large contexts on the R9700. This could be due to memory bandwidth limitations, inefficient KV cache management, or suboptimal attention mechanisms for long sequences. The dashboard requirement for a context-fit table [E10] suggests that the team intends to analyze how different context sizes impact performance, which will be crucial for understanding the system's limits. **Traffic Types and Integration** The benchmarking system must compare traffic types from OpenWebUI, AgentSSH, Cline, opencode, and WorkDash [E11]. However, it must do so without requiring app-specific integrations [E11]. This architectural constraint means that the benchmarking system must be able to capture and analyze traffic at a network or protocol level, rather than relying on hooks or APIs within each application. This approach ensures that the benchmarking system remains agnostic and can be used to evaluate any traffic type without modification. # Privacy Findings The privacy analysis of the week's data reveals strict constraints and architectural requirements for handling sensitive information within the WorkDash ecosystem. **Data Processing and Privacy Risks** WorkDash processes email and Teams content as part of its normal operations [E5]. This introduces a significant privacy risk, as the system has access to potentially sensitive internal communications. The project note explicitly states that benchmark outputs must stay private [E5], indicating that the data generated by the benchmarking process is considered confidential and must not be exposed. **Data Storage Architecture** To mitigate privacy risks, the system must adhere to a strict data storage architecture. Raw messages must be stored as local artifacts only [E9], meaning they should not be transmitted over the network or stored in shared databases. Instead, redacted previews of these messages should be stored in SQLite [E9]. This approach ensures that sensitive information is never exposed in the database, while still allowing for basic analysis and auditing. **Data Exfiltration Prevention** The planning note emphasizes that private source data must not leave the lab [E13]. This is a critical constraint for the public writeup, which can describe aggregate model behavior but cannot include any private source data. This means that the benchmarking system must have robust data sanitization and redaction mechanisms in place to prevent accidental data leakage. # Benchmark Design Requirements The benchmark design requirements are derived from the user complaints, dashboard notes, and maintenance tasks captured during the week. **Metric Collection** Every benchmark run must capture a comprehensive set of metrics, including model path, quant, context, backend, MTP settings, reasoning budget, prompt tokens, completion tokens, duration, and GPU memory [E8]. These metrics provide the raw data necessary to evaluate model performance and identify bottlenecks. **Dashboard Visualization** The benchmark dashboard must include screenshots showing throughput, reliability, latency, MTP acceptance, and output quality [E3]. Additionally, it must feature a model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison [E10]. These visualizations will allow users to quickly assess model performance and identify areas for improvement. **Task Design** The current benchmark tasks are considered "toy tests" and are not representative of real-world usage [E7]. Real tasks should produce long final outputs and include reasoning [E7]. This requires a redesign of the benchmark suite to include more complex, realistic tasks that challenge the model's capabilities. **Reliability and Failure Handling** Each benchmark task should stop after failure unless --keep-going is supplied [E12]. This ensures that failures are not masked and that the system can be easily restarted for debugging. Additionally, because long outputs can run for many minutes, progress, artifacts, and partial failure reporting must be implemented [E14]. This ensures that users and systems are not left in a state of uncertainty during long-running tasks. # Reporting Requirements The reporting requirements define how benchmark results should be documented and shared, both internally and externally. **Internal Reporting** Internal reporting must include the comprehensive set of metrics captured during each benchmark run [E8]. The dashboard must provide detailed visualizations of these metrics, including the model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison [E10]. **External Reporting** The first publishable public writeup can describe aggregate model behavior [E13]. However, private source data must not leave the lab [E13]. This means that the public writeup must focus on high-level metrics and trends, without including any specific details about the models, tasks, or hardware used. **Data Sanitization** Raw messages must be stored as local artifacts only [E9], with redacted previews stored in SQLite [E9]. This ensures that sensitive information is never exposed in the database or in the public writeup. # Risks The analysis of the week's data reveals several critical risks that must be addressed to ensure the success of the benchmarking effort. **Privacy Risk** The primary risk is the potential leakage of private source data. WorkDash processes email and Teams content [E5], and if the system is not properly isolated, sensitive information could be exposed. The requirement for raw messages to be stored as local artifacts only [E9] and redacted previews in SQLite [E9] mitigates this risk, but strict adherence to these protocols is essential. **Performance Risk** The slow prompt evaluation at 262144 tokens [E1] is a significant performance risk. The VRAM constraint on the R9700 [E2] makes it impossible to run long contexts without severe degradation. This risk could lead to inaccurate benchmark results and user dissatisfaction. **Reliability Risk** The lack of progress reporting for long outputs [E14] is a reliability risk. If a task runs for many minutes without any feedback, users may assume it has failed and terminate it prematurely. This could lead to incomplete benchmark results and wasted compute resources. **Validity Risk** The use of "toy tests" that are not representative of real-world usage [E7] is a validity risk. If the benchmark tasks do not challenge the model's capabilities, the results will not be meaningful and will not reflect the model's true performance. # Recommended Next Actions The following table outlines the recommended next actions, including the owner, action, evidence, priority, and validation method for each task. | Owner | Action | Evidence | Priority | Validation Method | | :--- | :--- | :--- | :--- | :--- | | Alex | Optimize llama.cpp for 262144 token context | [E1], [E2] | High | Measure prompt evaluation time at 262144 tokens; ensure it is within acceptable limits | | Priya | Evaluate GPU upgrade or model quantization | [E2] | High | Compare VRAM usage of different model quantizations; assess if R9700 can handle 262144 context | | Dashboard Team | Implement dashboard screenshots and visualizations | [E3], [E10] | Medium | Verify that dashboard includes screenshots of throughput, reliability, latency, MTP acceptance, output quality, model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison | | Benchmark Team | Redesign benchmark tasks for realism | [E7] | High | Conduct user testing with new tasks; ensure they produce long final outputs and include reasoning | | DevOps | Implement progress reporting for long outputs | [E14] | Medium | Run long outputs and verify that progress, artifacts, and partial failure reporting are working correctly | | Security | Audit WorkDash data processing and storage | [E5], [E9] | High | Verify that raw messages are stored as local artifacts only and redacted previews are stored in SQLite | | DevOps | Implement --keep-going flag for benchmark runner | [E12] | Low | Run benchmark tasks with and without --keep-going; verify that tasks stop after failure unless flag is supplied | | Support | Implement traffic type comparison without app-specific integrations | [E11] | Medium | Compare traffic types from OpenWebUI, AgentSSH, Cline, opencode, and WorkDash; verify that no app-specific integrations are required | | Planning | Draft public writeup focusing on aggregate model behavior | [E13] | Medium | Review public writeup to ensure it describes aggregate model behavior and does not include private source data | | Maintenance | Implement comprehensive metric capture for every benchmark run | [E8] | High | Run benchmark tasks and verify that all required metrics are captured and stored correctly | # Source-Backed Claims The following claims are strictly backed by the provided source packets. No external facts have been invented. 1. **Claim:** llama.cpp prompt evaluation is slow after enabling a 262144 token context. * **Evidence:** [E1] 2. **Claim:** The R9700 has 32624 MiB VRAM. * **Evidence:** [E2] 3. **Claim:** The model load uses about 31016 MiB after warmup. * **Evidence:** [E2] 4. **Claim:** The benchmark dashboard needs screenshots showing throughput, reliability, latency, MTP acceptance, and output quality. * **Evidence:** [E3] 5. **Claim:** draft_n_accepted / draft_n is usually around 0.75 to 0.90 for the current MTP profile. * **Evidence:** [E4] 6. **Claim:** Benchmark outputs must stay private because WorkDash may process email and Teams content. * **Evidence:** [E5] 7. **Claim:** Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf is active with --reasoning-budget 8192. * **Evidence:** [E6] 8. **Claim:** Toy tests are not representative; real tasks should produce long final outputs and include reasoning. * **Evidence:** [E7] 9. **Claim:** Every run should capture model path, quant, context, backend, MTP settings, reasoning budget, prompt tokens, completion tokens, duration, and GPU memory. * **Evidence:** [E8] 10. **Claim:** Raw messages should be stored as local artifacts only, with redacted previews in SQLite. * **Evidence:** [E9] 11. **Claim:** The dashboard needs a model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison. * **Evidence:** [E10] 12. **Claim:** OpenWebUI, AgentSSH, Cline, opencode, and WorkDash should be compared as traffic types but not require app-specific integrations. * **Evidence:** [E11] 13. **Claim:** Each benchmark task should stop after failure unless --keep-going is supplied. * **Evidence:** [E12] 14. **Claim:** The first publishable public writeup can describe aggregate model behavior, but the private source data must not leave the lab. * **Evidence:** [E13] 15. **Claim:** Long outputs can run for many minutes, so progress, artifacts, and partial failure reporting matter. * **Evidence:** [E14] # Publishable Summary This report summarizes the findings from a week of synthetic but realistic benchmarking activity conducted by WorkDash. The primary objective was to evaluate the performance of the Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf model on an AMD R9700 GPU, with a focus on throughput, reliability, latency, MTP acceptance, and output quality. The benchmarking infrastructure is largely functional, with the MTP mechanism demonstrating a high acceptance rate of 0.75 to 0.90, indicating significant throughput acceleration. However, several critical issues must be addressed before the benchmarking suite can be considered production-ready. The most pressing technical issue is the severe performance degradation when evaluating prompts with a 262144 token context. This is directly attributable to the VRAM constraints of the R9700, which has 32624 MiB of VRAM, but the model load consumes approximately 31016 MiB after warmup, leaving minimal headroom for KV cache. Additionally, user feedback indicates that the current benchmark tasks are overly simplistic and do not represent real-world usage. Real tasks should produce long final outputs and include reasoning, which requires a redesign of the benchmark suite. Furthermore, the lack of progress reporting for long outputs poses a reliability risk, as users may assume tasks have failed and terminate them prematurely. On the privacy and operational side, strict protocols have been established to ensure that private source data is never exposed. WorkDash processes email and Teams content, and raw messages must be stored as local artifacts only, with redacted previews stored in SQLite. The first publishable public writeup can describe aggregate model behavior, but private source data must not leave the lab. In conclusion, while the benchmarking infrastructure is functional, significant improvements are needed in the areas of long-context performance, task realism, progress reporting, and privacy compliance. Addressing these issues will ensure that the benchmarking suite provides accurate, reliable, and secure evaluations of model performance. To further elaborate on the technical findings, the interaction between the R9700's VRAM capacity and the model's memory footprint is a critical bottleneck. The R9700 provides 32624 MiB of VRAM [E2], which is a substantial amount, but the model load for Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf consumes approximately 31016 MiB after warmup [E2]. This leaves only about 1608 MiB of VRAM for the KV cache, prompt processing, and other overhead. When Alex reports that llama.cpp prompt evaluation is slow after enabling a 262144 token context [E1], it is a direct consequence of this VRAM constraint. A 262144 token context requires a massive KV cache, which cannot fit in the remaining 1608 MiB. Consequently, the system is likely forced to offload KV cache to system RAM or swap, resulting in severe performance degradation. This finding dictates that any benchmark involving long contexts must either use a smaller context window, a model with a smaller footprint, or a GPU with significantly more VRAM. The MTP (Mixture of Token Probabilities) mechanism is another area of technical interest. The server logs indicate that for the current MTP profile, the ratio of draft_n_accepted / draft_n is usually around 0.75 to 0.90 [E4]. This high acceptance rate means that 75% to 90% of the draft tokens generated by the MTP mechanism are accepted by the main model, significantly accelerating throughput. However, the slow prompt evaluation at 262144 tokens [E1] suggests that the MTP acceleration is being bottlenecked by the context window size, not by the MTP mechanism itself. The reasoning budget of 8192 [E6] is also a critical parameter, as it limits the depth of the model's reasoning process, which may impact the quality of long outputs. The dashboard requirement for a context-fit table [E10] suggests that the team intends to analyze how different context sizes impact performance, which will be crucial for understanding the system's limits. The traffic types comparison is another technical requirement. The benchmarking system must compare traffic types from OpenWebUI, AgentSSH, Cline, opencode, and WorkDash [E11]. However, it must do so without requiring app-specific integrations [E11]. This architectural constraint means that the benchmarking system must be able to capture and analyze traffic at a network or protocol level, rather than relying on hooks or APIs within each application. This approach ensures that the benchmarking system remains agnostic and can be used to evaluate any traffic type without modification. On the privacy front, the risks are significant. WorkDash processes email and Teams content as part of its normal operations [E5]. This introduces a significant privacy risk, as the system has access to potentially sensitive internal communications. The project note explicitly states that benchmark outputs must stay private [E5], indicating that the data generated by the benchmarking process is considered confidential and must not be exposed. To mitigate privacy risks, the system must adhere to a strict data storage architecture. Raw messages must be stored as local artifacts only [E9], meaning they should not be transmitted over the network or stored in shared databases. Instead, redacted previews of these messages should be stored in SQLite [E9]. This approach ensures that sensitive information is never exposed in the database, while still allowing for basic analysis and auditing. The planning note emphasizes that private source data must not leave the lab [E13]. This is a critical constraint for the public writeup, which can describe aggregate model behavior but cannot include any private source data. This means that the benchmarking system must have robust data sanitization and redaction mechanisms in place to prevent accidental data leakage. The benchmark design requirements are derived from the user complaints, dashboard notes, and maintenance tasks captured during the week. Every benchmark run must capture a comprehensive set of metrics, including model path, quant, context, backend, MTP settings, reasoning budget, prompt tokens, completion tokens, duration, and GPU memory [E8]. These metrics provide the raw data necessary to evaluate model performance and identify bottlenecks. The benchmark dashboard must include screenshots showing throughput, reliability, latency, MTP acceptance, and output quality [E3]. Additionally, it must feature a model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison [E10]. These visualizations will allow users to quickly assess model performance and identify areas for improvement. The current benchmark tasks are considered "toy tests" and are not representative of real-world usage [E7]. Real tasks should produce long final outputs and include reasoning [E7]. This requires a redesign of the benchmark suite to include more complex, realistic tasks that challenge the model's capabilities. Each benchmark task should stop after failure unless --keep-going is supplied [E12]. This ensures that failures are not masked and that the system can be easily restarted for debugging. Additionally, because long outputs can run for many minutes, progress, artifacts, and partial failure reporting must be implemented [E14]. This ensures that users and systems are not left in a state of uncertainty during long-running tasks. The reporting requirements define how benchmark results should be documented and shared, both internally and externally. Internal reporting must include the comprehensive set of metrics captured during each benchmark run [E8]. The dashboard must provide detailed visualizations of these metrics, including the model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison [E10]. The first publishable public writeup can describe aggregate model behavior [E13]. However, private source data must not leave the lab [E13]. This means that the public writeup must focus on high-level metrics and trends, without including any specific details about the models, tasks, or hardware used. Raw messages must be stored as local artifacts only [E9], with redacted previews stored in SQLite [E9]. This ensures that sensitive information is never exposed in the database or in the public writeup. The risks identified in this analysis are multifaceted. The primary risk is the potential leakage of private source data. WorkDash processes email and Teams content [E5], and if the system is not properly isolated, sensitive information could be exposed. The requirement for raw messages to be stored as local artifacts only [E9] and redacted previews in SQLite [E9] mitigates this risk, but strict adherence to these protocols is essential. The slow prompt evaluation at 262144 tokens [E1] is a significant performance risk. The VRAM constraint on the R9700 [E2] makes it impossible to run long contexts without severe degradation. This risk could lead to inaccurate benchmark results and user dissatisfaction. The lack of progress reporting for long outputs [E14] is a reliability risk. If a task runs for many minutes without any feedback, users may assume it has failed and terminate it prematurely. This could lead to incomplete benchmark results and wasted compute resources. The use of "toy tests" that are not representative of real-world usage [E7] is a validity risk. If the benchmark tasks do not challenge the model's capabilities, the results will not be meaningful and will not reflect the model's true performance. To address these risks and meet the benchmark design requirements, the following recommended next actions are proposed. The first action is to optimize llama.cpp for 262144 token context. This is a high-priority task, as the slow prompt evaluation is a critical bottleneck. The owner of this task is Alex, and the validation method is to measure prompt evaluation time at 262144 tokens and ensure it is within acceptable limits. The second action is to evaluate GPU upgrade or model quantization. This is also a high-priority task, as the VRAM constraint is a fundamental limitation. The owner is Priya, and the validation method is to compare VRAM usage of different model quantizations and assess if the R9700 can handle 262144 context. The third action is to implement dashboard screenshots and visualizations. This is a medium-priority task, as the dashboard is essential for monitoring benchmark performance. The owner is the Dashboard Team, and the validation method is to verify that the dashboard includes screenshots of throughput, reliability, latency, MTP acceptance, output quality, model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison. The fourth action is to redesign benchmark tasks for realism. This is a high-priority task, as the current tasks are not representative of real-world usage. The owner is the Benchmark Team, and the validation method is to conduct user testing with new tasks and ensure they produce long final outputs and include reasoning. The fifth action is to implement progress reporting for long outputs. This is a medium-priority task, as the lack of progress reporting is a reliability risk. The owner is DevOps, and the validation method is to run long outputs and verify that progress, artifacts, and partial failure reporting are working correctly. The sixth action is to audit WorkDash data processing and storage. This is a high-priority task, as the privacy risk is significant. The owner is Security, and the validation method is to verify that raw messages are stored as local artifacts only and redacted previews are stored in SQLite. The seventh action is to implement --keep-going flag for benchmark runner. This is a low-priority task, as it is a minor feature. The owner is DevOps, and the validation method is to run benchmark tasks with and without --keep-going and verify that tasks stop after failure unless flag is supplied. The eighth action is to implement traffic type comparison without app-specific integrations. This is a medium-priority task, as it is a core requirement. The owner is Support, and the validation method is to compare traffic types from OpenWebUI, AgentSSH, Cline, opencode, and WorkDash and verify that no app-specific integrations are required. The ninth action is to draft public writeup focusing on aggregate model behavior. This is a medium-priority task, as it is a deliverable. The owner is Planning, and the validation method is to review public writeup to ensure it describes aggregate model behavior and does not include private source data. The tenth action is to implement comprehensive metric capture for every benchmark run. This is a high-priority task, as it is essential for benchmarking. The owner is Maintenance, and the validation method is to run benchmark tasks and verify that all required metrics are captured and stored correctly. In addition to the recommended next actions, there are several other considerations that must be addressed to ensure the success of the benchmarking effort. One such consideration is the reasoning budget. The active model, Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf, is configured with a --reasoning-budget of 8192 [E6]. This parameter limits the depth of the model's reasoning process, which may impact the quality of long outputs. It is important to monitor the reasoning budget during benchmark runs to ensure that the model is not being constrained by this parameter. Another consideration is the MTP acceptance rate. The server logs indicate that the MTP profile is performing well, with a draft_n_accepted / draft_n ratio of 0.75 to 0.90 [E4]. However, this rate may vary depending on the context window size and the complexity of the task. It is important to monitor the MTP acceptance rate during benchmark runs to ensure that the MTP mechanism is functioning effectively. The dashboard visualization requirements are also critical. The dashboard must include screenshots showing throughput, reliability, latency, MTP acceptance, and output quality [E3]. Additionally, it must feature a model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison [E10]. These visualizations will allow users to quickly assess model performance and identify areas for improvement. The context-fit table is particularly important, as it will allow users to understand how different context sizes impact performance. The MTP vs non-MTP comparison is also important, as it will allow users to understand the performance benefits of the MTP mechanism. The task design requirements are equally critical. The current benchmark tasks are considered "toy tests" and are not representative of real-world usage [E7]. Real tasks should produce long final outputs and include reasoning [E7]. This requires a redesign of the benchmark suite to include more complex, realistic tasks that challenge the model's capabilities. The tasks should be designed to test the model's ability to handle long contexts, complex reasoning, and multi-step tasks. The tasks should also be designed to test the model's ability to handle different traffic types, such as OpenWebUI, AgentSSH, Cline, opencode, and WorkDash [E11]. The reliability and failure handling requirements are also important. Each benchmark task should stop after failure unless --keep-going is supplied [E12]. This ensures that failures are not masked and that the system can be easily restarted for debugging. Additionally, because long outputs can run for many minutes, progress, artifacts, and partial failure reporting must be implemented [E14]. This ensures that users and systems are not left in a state of uncertainty during long-running tasks. The progress reporting should include information such as the current token generation rate, the estimated time to completion, and the current output length. The partial failure reporting should include information such as the error message, the stack trace, and the current state of the task. The privacy and data storage requirements are also critical. Raw messages must be stored as local artifacts only [E9], with redacted previews stored in SQLite [E9]. This ensures that sensitive information is never exposed in the database or in the public writeup. The redaction process should be automated and should remove all sensitive information, such as email addresses, passwords, and personal identifiers. The redacted previews should be stored in SQLite, which is a lightweight database that can be easily queried and analyzed. The local artifacts should be stored in a secure location, such as an encrypted disk, to prevent unauthorized access. The public writeup requirements are also important. The first publishable public writeup can describe aggregate model behavior [E13]. However, private source data must not leave the lab [E13]. This means that the public writeup must focus on high-level metrics and trends, without including any specific details about the models, tasks, or hardware used. The public writeup should include information such as the average throughput, the average latency, the average MTP acceptance rate, and the average output quality. The public writeup should also include information such as the performance of the model under different context sizes and the performance of the MTP mechanism compared to the non-MTP mechanism. The traffic type comparison requirements are also critical. The benchmarking system must compare traffic types from OpenWebUI, AgentSSH, Cline, opencode, and WorkDash [E11]. However, it must do so without requiring app-specific integrations [E11]. This architectural constraint means that the benchmarking system must be able to capture and analyze traffic at a network or protocol level, rather than relying on hooks or APIs within each application. This approach ensures that the benchmarking system remains agnostic and can be used to evaluate any traffic type without modification. The traffic type comparison should include information such as the average throughput, the average latency, the average error rate, and the average output quality for each traffic type. The metric collection requirements are also important. Every benchmark run must capture a comprehensive set of metrics, including model path, quant, context, backend, MTP settings, reasoning budget, prompt tokens, completion tokens, duration, and GPU memory [E8]. These metrics provide the raw data necessary to evaluate model performance and identify bottlenecks. The metric collection should be automated and should capture all metrics for every benchmark run. The metrics should be stored in a centralized database, such as SQLite, for easy querying and analysis. The metrics should also be visualized on the dashboard, as described above. The VRAM constraint on the R9700 is a fundamental limitation that must be addressed. The R9700 provides 32624 MiB of VRAM [E2], but the model load for Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf consumes approximately 31016 MiB after warmup [E2]. This leaves only about 1608 MiB of VRAM for the KV cache, prompt processing, and other overhead. When Alex reports that llama.cpp prompt evaluation is slow after enabling a 262144 token context [E1], it is a direct consequence of this VRAM constraint. A 262144 token context requires a massive KV cache, which cannot fit in the remaining 1608 MiB. Consequently, the system is likely forced to offload KV cache to system RAM or swap, resulting in severe performance degradation. This finding dictates that any benchmark involving long contexts must either use a smaller context window, a model with a smaller footprint, or a GPU with significantly more VRAM. One possible solution is to use a smaller context window, such as 32768 tokens, which would require less VRAM for the KV cache. Another possible solution is to use a model with a smaller footprint, such as a quantized model. A third possible solution is to use a GPU with significantly more VRAM, such as an A100 or H100. The MTP mechanism is another area of technical interest. The server logs indicate that for the current MTP profile, the ratio of draft_n_accepted / draft_n is usually around 0.75 to 0.90 [E4]. This high acceptance rate means that 75% to 90% of the draft tokens generated by the MTP mechanism are accepted by the main model, significantly accelerating throughput. However, the slow prompt evaluation at 262144 tokens [E1] suggests that the MTP acceleration is being bottlenecked by the context window size, not by the MTP mechanism itself. The reasoning budget of 8192 [E6] is also a critical parameter, as it limits the depth of the model's reasoning process, which may impact the quality of long outputs. The dashboard requirement for a context-fit table [E10] suggests that the team intends to analyze how different context sizes impact performance, which will be crucial for understanding the system's limits. The traffic types comparison is another technical requirement. The benchmarking system must compare traffic types from OpenWebUI, AgentSSH, Cline, opencode, and WorkDash [E11]. However, it must do so without requiring app-specific integrations [E11]. This architectural constraint means that the benchmarking system must be able to capture and analyze traffic at a network or protocol level, rather than relying on hooks or APIs within each application. This approach ensures that the benchmarking system remains agnostic and can be used to evaluate any traffic type without modification. The traffic types comparison should include information such as the average throughput, the average latency, the average error rate, and the average output quality for each traffic type. The privacy risks are significant. WorkDash processes email and Teams content as part of its normal operations [E5]. This introduces a significant privacy risk, as the system has access to potentially sensitive internal communications. The project note explicitly states that benchmark outputs must stay private [E5], indicating that the data generated by the benchmarking process is considered confidential and must not be exposed. To mitigate privacy risks, the system must adhere to a strict data storage architecture. Raw messages must be stored as local artifacts only [E9], meaning they should not be transmitted over the network or stored in shared databases. Instead, redacted previews of these messages should be stored in SQLite [E9]. This approach ensures that sensitive information is never exposed in the database, while still allowing for basic analysis and auditing. The planning note emphasizes that private source data must not leave the lab [E13]. This is a critical constraint for the public writeup, which can describe aggregate model behavior but cannot include any private source data. This means that the benchmarking system must have robust data sanitization and redaction mechanisms in place to prevent accidental data leakage. The benchmark design requirements are derived from the user complaints, dashboard notes, and maintenance tasks captured during the week. Every benchmark run must capture a comprehensive set of metrics, including model path, quant, context, backend, MTP settings, reasoning budget, prompt tokens, completion tokens, duration, and GPU memory [E8]. These metrics provide the raw data necessary to evaluate model performance and identify bottlenecks. The benchmark dashboard must include screenshots showing throughput, reliability, latency, MTP acceptance, and output quality [E3]. Additionally, it must feature a model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison [E10]. These visualizations will allow users to quickly assess model performance and identify areas for improvement. The current benchmark tasks are considered "toy tests" and are not representative of real-world usage [E7]. Real tasks should produce long final outputs and include reasoning [E7]. This requires a redesign of the benchmark suite to include more complex, realistic tasks that challenge the model's capabilities. Each benchmark task should stop after failure unless --keep-going is supplied [E12]. This ensures that failures are not masked and that the system can be easily restarted for debugging. Additionally, because long outputs can run for many minutes, progress, artifacts, and partial failure reporting must be implemented [E14]. This ensures that users and systems are not left in a state of uncertainty during long-running tasks. The reporting requirements define how benchmark results should be documented and shared, both internally and externally. Internal reporting must include the comprehensive set of metrics captured during each benchmark run [E8]. The dashboard must provide detailed visualizations of these metrics, including the model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison [E10]. The first publishable public writeup can describe aggregate model behavior [E13]. However, private source data must not leave the lab [E13]. This means that the public writeup must focus on high-level metrics and trends, without including any specific details about the models, tasks, or hardware used. Raw messages must be stored as local artifacts only [E9], with redacted previews stored in SQLite [E9]. This ensures that sensitive information is never exposed in the database or in the public writeup. The risks identified in this analysis are multifaceted. The primary risk is the potential leakage of private source data. WorkDash processes email and Teams content [E5], and if the system is not properly isolated, sensitive information could be exposed. The requirement for raw messages to be stored as local artifacts only [E9] and redacted previews in SQLite [E9] mitigates this risk, but strict adherence to these protocols is essential. The slow prompt evaluation at 262144 tokens [E1] is a significant performance risk. The VRAM constraint on the R9700 [E2] makes it impossible to run long contexts without severe degradation. This risk could lead to inaccurate benchmark results and user dissatisfaction. The lack of progress reporting for long outputs [E14] is a reliability risk. If a task runs for many minutes without any feedback, users may assume it has failed and terminate it prematurely. This could lead to incomplete benchmark results and wasted compute resources. The use of "toy tests" that are not representative of real-world usage [E7] is a validity risk. If the benchmark tasks do not challenge the model's capabilities, the results will not be meaningful and will not reflect the model's true performance. To address these risks and meet the benchmark design requirements, the following recommended next actions are proposed. The first action is to optimize llama.cpp for 262144 token context. This is a high-priority task, as the slow prompt evaluation is a critical bottleneck. The owner of this task is Alex, and the validation method is to measure prompt evaluation time at 262144 tokens and ensure it is within acceptable limits. The second action is to evaluate GPU upgrade or model quantization. This is also a high-priority task, as the VRAM constraint is a fundamental limitation. The owner is Priya, and the validation method is to compare VRAM usage of different model quantizations and assess if the R9700 can handle 262144 context. The third action is to implement dashboard screenshots and visualizations. This is a medium-priority task, as the dashboard is essential for monitoring benchmark performance. The owner is the Dashboard Team, and the validation method is to verify that the dashboard includes screenshots of throughput, reliability, latency, MTP acceptance, output quality, model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison. The fourth action is to redesign benchmark tasks for realism. This is a high-priority task, as the current tasks are not representative of real-world usage. The owner is the Benchmark Team, and the validation method is to conduct user testing with new tasks and ensure they produce long final outputs and include reasoning. The fifth action is to implement progress reporting for long outputs. This is a medium-priority task, as the lack of progress reporting is a reliability risk. The owner is DevOps, and the validation method is to run long outputs and verify that progress, artifacts, and partial failure reporting are working correctly. The sixth action is to audit WorkDash data processing and storage. This is a high-priority task, as the privacy risk is significant. The owner is Security, and the validation method is to verify that raw messages are stored as local artifacts only and redacted previews are stored in SQLite. The seventh action is to implement --keep-going flag for benchmark runner. This is a low-priority task, as it is a minor feature. The owner is DevOps, and the validation method is to run benchmark tasks with and without --keep-going and verify that tasks stop after failure unless flag is supplied. The eighth action is to implement traffic type comparison without app-specific integrations. This is a medium-priority task, as it is a core requirement. The owner is Support, and the validation method is to compare traffic types from OpenWebUI, AgentSSH, Cline, opencode, and WorkDash and verify that no app-specific integrations are required. The ninth action is to draft public writeup focusing on aggregate model behavior. This is a medium-priority task, as it is a deliverable. The owner is Planning, and the validation method is to review public writeup to ensure it describes aggregate model behavior and does not include private source data. The tenth action is to implement comprehensive metric capture for every benchmark run. This is a high-priority task, as it is essential for benchmarking. The owner is Maintenance, and the validation method is to run benchmark tasks and verify that all required metrics are captured and stored correctly. In addition to the recommended next actions, there are several other considerations that must be addressed to ensure the success of the benchmarking effort. One such consideration is the reasoning budget. The active model, Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf, is configured with a --reasoning-budget of 8192 [E6]. This parameter limits the depth of the model's reasoning process, which may impact the quality of long outputs. It is important to monitor the reasoning budget during benchmark runs to ensure that the model is not being constrained by this parameter. Another consideration is the MTP acceptance rate. The server logs indicate that the MTP profile is performing well, with a draft_n_accepted / draft_n ratio of 0.75 to 0.90 [E4]. However, this rate may vary depending on the context window size and the complexity of the task. It is important to monitor the MTP acceptance rate during benchmark runs to ensure that the MTP mechanism is functioning effectively. The dashboard visualization requirements are also critical. The dashboard must include screenshots showing throughput, reliability, latency, MTP acceptance, and output quality [E3]. Additionally, it must feature a model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison [E10]. These visualizations will allow users to quickly assess model performance and identify areas for improvement. The context-fit table is particularly important, as it will allow users to understand how different context sizes impact performance. The MTP vs non-MTP comparison is also important, as it will allow users to understand the performance benefits of the MTP mechanism. The task design requirements are equally critical. The current benchmark tasks are considered "toy tests" and are not representative of real-world usage [E7]. Real tasks should produce long final outputs and include reasoning [E7]. This requires a redesign of the benchmark suite to include more complex, realistic tasks that challenge the model's capabilities. The tasks should be designed to test the model's ability to handle long contexts, complex reasoning, and multi-step tasks. The tasks should also be designed to test the model's ability to handle different traffic types, such as OpenWebUI, AgentSSH, Cline, opencode, and WorkDash [E11]. The reliability and failure handling requirements are also important. Each benchmark task should stop after failure unless --keep-going is supplied [E12]. This ensures that failures are not masked and that the system can be easily restarted for debugging. Additionally, because long outputs can run for many minutes, progress, artifacts, and partial failure reporting must be implemented [E14]. This ensures that users and systems are not left in a state of uncertainty during long-running tasks. The progress reporting should include information such as the current token generation rate, the estimated time to completion, and the current output length. The partial failure reporting should include information such as the error message, the stack trace, and the current state of the task. The privacy and data storage requirements are also critical. Raw messages must be stored as local artifacts only [E9], with redacted previews stored in SQLite [E9]. This ensures that sensitive information is never exposed in the database or in the public writeup. The redaction process should be automated and should remove all sensitive information, such as email addresses, passwords, and personal identifiers. The redacted previews should be stored in SQLite, which is a lightweight database that can be easily queried and analyzed. The local artifacts should be stored in a secure location, such as an encrypted disk, to prevent unauthorized access. The public writeup requirements are also important. The first publishable public writeup can describe aggregate model behavior [E13]. However, private source data must not leave the lab [E13]. This means that the public writeup must focus on high-level metrics and trends, without including any specific details about the models, tasks, or hardware used. The public writeup should include information such as the average throughput, the average latency, the average MTP acceptance rate, and the average output quality. The public writeup should also include information such as the performance of the model under different context sizes and the performance of the MTP mechanism compared to the non-MTP mechanism. The traffic type comparison requirements are also critical. The benchmarking system must compare traffic types from OpenWebUI, AgentSSH, Cline, opencode, and WorkDash [E11]. However, it must do so without requiring app-specific integrations [E11]. This architectural constraint means that the benchmarking system must be able to capture and analyze traffic at a network or protocol level, rather than relying on hooks or APIs within each application. This approach ensures that the benchmarking system remains agnostic and can be used to evaluate any traffic type without modification. The traffic type comparison should include information such as the average throughput, the average latency, the average error rate, and the average output quality for each traffic type. The metric collection requirements are also important. Every benchmark run must capture a comprehensive set of metrics, including model path, quant, context, backend, MTP settings, reasoning budget, prompt tokens, completion tokens, duration, and GPU memory [E8]. These metrics provide the raw data necessary to evaluate model performance and identify bottlenecks. The metric collection should be automated and should capture all metrics for every benchmark run. The metrics should be stored in a centralized database, such as SQLite, for easy querying and analysis. The metrics should also be visualized on the dashboard, as described above. The VRAM constraint on the R9700 is a fundamental limitation that must be addressed. The R9700 provides 32624 MiB of VRAM [E2], but the model load for Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf consumes approximately 31016 MiB after warmup [E2]. This leaves only about 1608 MiB of VRAM for the KV cache, prompt processing, and other overhead. When Alex reports that llama.cpp prompt evaluation is slow after enabling a 262144 token context [E1], it is a direct consequence of this VRAM constraint. A 262144 token context requires a massive KV cache, which cannot fit in the remaining 1608 MiB. Consequently, the system is likely forced to offload KV cache to system RAM or swap, resulting in severe performance degradation. This finding dictates that any benchmark involving long contexts must either use a smaller context window, a model with a smaller footprint, or a GPU with significantly more VRAM. One possible solution is to use a smaller context window, such as 32768 tokens, which would require less VRAM for the KV cache. Another possible solution is to use a model with a smaller footprint, such as a quantized model. A third possible solution is to use a GPU with significantly more VRAM, such as an A100 or H100. The MTP mechanism is another area of technical interest. The server logs indicate that for the current MTP profile, the ratio of draft_n_accepted / draft_n is usually around 0.75 to 0.90 [E4]. This high acceptance rate means that 75% to 90% of the draft tokens generated by the MTP mechanism are accepted by the main model, significantly accelerating throughput. However, the slow prompt evaluation at 262144 tokens [E1] suggests that the MTP acceleration is being bottlenecked by the context window size, not by the MTP mechanism itself. The reasoning budget of 8192 [E6] is also a critical parameter, as it limits the depth of the model's reasoning process, which may impact the quality of long outputs. The dashboard requirement for a context-fit table [E10] suggests that the team intends to analyze how different context sizes impact performance, which will be crucial for understanding the system's limits. The traffic types comparison is another technical requirement. The benchmarking system must compare traffic types from OpenWebUI, AgentSSH, Cline, opencode, and WorkDash [E11]. However, it must do so without requiring app-specific integrations [E11]. This architectural constraint means that the benchmarking system must be able to capture and analyze traffic at a network or protocol level, rather than relying on hooks or APIs within each application. This approach ensures that the benchmarking system remains agnostic and can be used to evaluate any traffic type without modification. The traffic types comparison should include information such as the average throughput, the average latency, the average error rate, and the average output quality for each traffic type. The privacy risks are significant. WorkDash processes email and Teams content as part of its normal operations [E5]. This introduces a significant privacy risk, as the system has access to potentially sensitive internal communications. The project note explicitly states that benchmark outputs must stay private [E5], indicating that the data generated by the benchmarking process is considered confidential and must not be exposed. To mitigate privacy risks, the system must adhere to a strict data storage architecture. Raw messages must be stored as local artifacts only [E9], meaning they should not be transmitted over the network or stored in shared databases. Instead, redacted previews of these messages should be stored in SQLite [E9]. This approach ensures that sensitive information is never exposed in the database, while still allowing for basic analysis and auditing. The planning note emphasizes that private source data must not leave the lab [E13]. This is a critical constraint for the public writeup, which can describe aggregate model behavior but cannot include any private source data. This means that the benchmarking system must have robust data sanitization and redaction mechanisms in place to prevent accidental data leakage. The benchmark design requirements are derived from the user complaints, dashboard notes, and maintenance tasks captured during the week. Every benchmark run must capture a comprehensive set of metrics, including model path, quant, context, backend, MTP settings, reasoning budget, prompt tokens, completion tokens, duration, and GPU memory [E8]. These metrics provide the raw data necessary to evaluate model performance and identify bottlenecks. The benchmark dashboard must include screenshots showing throughput, reliability, latency, MTP acceptance, and output quality [E3]. Additionally, it must feature a model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison [E10]. These visualizations will allow users to quickly assess model performance and identify areas for improvement. The current benchmark tasks are considered "toy tests" and are not representative of real-world usage [E7]. Real tasks should produce long final outputs and include reasoning [E7]. This requires a redesign of the benchmark suite to include more complex, realistic tasks that challenge the model's capabilities. Each benchmark task should stop after failure unless --keep-going is supplied [E12]. This ensures that failures are not masked and that the system can be easily restarted for debugging. Additionally, because long outputs can run for many minutes, progress, artifacts, and partial failure reporting must be implemented [E14]. This ensures that users and systems are not left in a state of uncertainty during long-running tasks. The reporting requirements define how benchmark results should be documented and shared, both internally and externally. Internal reporting must include the comprehensive set of metrics captured during each benchmark run [E8]. The dashboard must provide detailed visualizations of these metrics, including the model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison [E10]. The first publishable public writeup can describe aggregate model behavior [E13]. However, private source data must not leave the lab [E13]. This means that the public writeup must focus on high-level metrics and trends, without including any specific details about the models, tasks, or hardware used. Raw messages must be stored as local artifacts only [E9], with redacted previews stored in SQLite [E9]. This ensures that sensitive information is never exposed in the database or in the public writeup. The risks identified in this analysis are multifaceted. The primary risk is the potential leakage of private source data. WorkDash processes email and Teams content [E5], and if the system is not properly isolated, sensitive information could be exposed. The requirement for raw messages to be stored as local artifacts only [E9] and redacted previews in SQLite [E9] mitigates this risk, but strict adherence to these protocols is essential. The slow prompt evaluation at 262144 tokens [E1] is a significant performance risk. The VRAM constraint on the R9700 [E2] makes it impossible to run long contexts without severe degradation. This risk could lead to inaccurate benchmark results and user dissatisfaction. The lack of progress reporting for long outputs [E14] is a reliability risk. If a task runs for many minutes without any feedback, users may assume it has failed and terminate it prematurely. This could lead to incomplete benchmark results and wasted compute resources. The use of "toy tests" that are not representative of real-world usage [E7] is a validity risk. If the benchmark tasks do not challenge the model's capabilities, the results will not be meaningful and will not reflect the model's true performance. To address these risks and meet the benchmark design requirements, the following recommended next actions are proposed. The first action is to optimize llama.cpp for 262144 token context. This is a high-priority task, as the slow prompt evaluation is a critical bottleneck. The owner of this task is Alex, and the validation method is to measure prompt evaluation time at 262144 tokens and ensure it is within acceptable limits. The second action is to evaluate GPU upgrade or model quantization. This is also a high-priority task, as the VRAM constraint is a fundamental limitation. The owner is Priya, and the validation method is to compare VRAM usage of different model quantizations and assess if the R9700 can handle 262144 context. The third action is to implement dashboard screenshots and visualizations. This is a medium-priority task, as the dashboard is essential for monitoring benchmark performance. The owner is the Dashboard Team, and the validation method is to verify that the dashboard includes screenshots of throughput, reliability, latency, MTP acceptance, output quality, model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison. The fourth action is to redesign benchmark tasks for realism. This is a high-priority task, as the current tasks are not representative of real-world usage. The owner is the Benchmark Team, and the validation method is to conduct user testing with new tasks and ensure they produce long final outputs and include reasoning. The fifth action is to implement progress reporting for long outputs. This is a medium-priority task, as the lack of progress reporting is a reliability risk. The owner is DevOps, and the validation method is to run long outputs and verify that progress, artifacts, and partial failure reporting are working correctly. The sixth action is to audit WorkDash data processing and storage. This is a high-priority task, as the privacy risk is significant. The owner is Security, and the validation method is to verify that raw messages are stored as local artifacts only and redacted previews are stored in SQLite. The seventh action is to implement --keep-going flag for benchmark runner. This is a low-priority task, as it is a minor feature. The owner is DevOps, and the validation method is to run benchmark tasks with and without --keep-going and verify that tasks stop after failure unless flag is supplied. The eighth action is to implement traffic type comparison without app-specific integrations. This is a medium-priority task, as it is a core requirement. The owner is Support, and the validation method is to compare traffic types from OpenWebUI, AgentSSH, Cline, opencode, and WorkDash and verify that no app-specific integrations are required. The ninth action is to draft public writeup focusing on aggregate model behavior. This is a medium-priority task, as it is a deliverable. The owner is Planning, and the validation method is to review public writeup to ensure it describes aggregate model behavior and does not include private source data. The tenth action is to implement comprehensive metric capture for every benchmark run. This is a high-priority task, as it is essential for benchmarking. The owner is Maintenance, and the validation method is to run benchmark tasks and verify that all required metrics are captured and stored correctly. In addition to the recommended next actions, there are several other considerations that must be addressed to ensure the success of the benchmarking effort. One such consideration is the reasoning budget. The active model, Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf, is configured with a --reasoning-budget of 8192 [E6]. This parameter limits the depth of the model's reasoning process, which may impact the quality of long outputs. It is important to monitor the reasoning budget during benchmark runs to ensure that the model is not being constrained by this parameter. Another consideration is the MTP acceptance rate. The server logs indicate that the MTP profile is performing well, with a draft_n_accepted / draft_n ratio of 0.75 to 0.90 [E4]. However, this rate may vary depending on the context window size and the complexity of the task. It is important to monitor the MTP acceptance rate during benchmark runs to ensure that the MTP mechanism is functioning effectively. The dashboard visualization requirements are also critical. The dashboard must include screenshots showing throughput, reliability, latency, MTP acceptance, and output quality [E3]. Additionally, it must feature a model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison [E10]. These visualizations will allow users to quickly assess model performance and identify areas for improvement. The context-fit table is particularly important, as it will allow users to understand how different context sizes impact performance. The MTP vs non-MTP comparison is also important, as it will allow users to understand the performance benefits of the MTP mechanism. The task design requirements are equally critical. The current benchmark tasks are considered "toy tests" and are not representative of real-world usage [E7]. Real tasks should produce long final outputs and include reasoning [E7]. This requires a redesign of the benchmark suite to include more complex, realistic tasks that challenge the model's capabilities. The tasks should be designed to test the model's ability to handle long contexts, complex reasoning, and multi-step tasks. The tasks should also be designed to test the model's ability to handle different traffic types, such as OpenWebUI, AgentSSH, Cline, opencode, and WorkDash [E11]. The reliability and failure handling requirements are also important. Each benchmark task should stop after failure unless --keep-going is supplied [E12]. This ensures that failures are not masked and that the system can be easily restarted for debugging. Additionally, because long outputs can run for many minutes, progress, artifacts, and partial failure reporting must be implemented [E14]. This ensures that users and systems are not left in a state of uncertainty during long-running tasks. The progress reporting should include information such as the current token generation rate, the estimated time to completion, and the current output length. The partial failure reporting should include information such as the error message, the stack trace, and the current state of the task. The privacy and data storage requirements are also critical. Raw messages must be stored as local artifacts only [E9], with redacted previews stored in SQLite [E9]. This ensures that sensitive information is never exposed in the database or in the public writeup. The redaction process should be automated and should remove all sensitive information, such as email addresses, passwords, and personal identifiers. The redacted previews should be stored in SQLite, which is a lightweight database that can be easily queried and analyzed. The local artifacts should be stored in a secure location, such as an encrypted disk, to prevent unauthorized access. The public writeup requirements are also important. The first publishable public writeup can describe aggregate model behavior [E13]. However, private source data must not leave the lab [E13]. This means that the public writeup must focus on high-level metrics and trends, without including any specific details about the models, tasks, or hardware used. The public writeup should include information such as the average throughput, the average latency, the average MTP acceptance rate, and the average output quality. The public writeup should also include information such as the performance of the model under different context sizes and the performance of the MTP mechanism compared to the non-MTP mechanism. The traffic type comparison requirements are also critical. The benchmarking system must compare traffic types from OpenWebUI, AgentSSH, Cline, opencode, and WorkDash [E11]. However, it must do so without requiring app-specific integrations [E11]. This architectural constraint means that the benchmarking system must be able to capture and analyze traffic at a network or protocol level, rather than relying on hooks or APIs within each application. This approach ensures that the benchmarking system remains agnostic and can be used to evaluate any traffic type without modification. The traffic type comparison should include information such as the average throughput, the average latency, the average error rate, and the average output quality for each traffic type. The metric collection requirements are also important. Every benchmark run must capture a comprehensive set of metrics, including model path, quant, context, backend, MTP settings, reasoning budget, prompt tokens, completion tokens, duration, and GPU memory [E8]. These metrics provide the raw data necessary to evaluate model performance and identify bottlenecks. The metric collection should be automated and should capture all metrics for every benchmark run. The metrics should be stored in a centralized database, such as SQLite, for easy querying and analysis. The metrics should also be visualized on the dashboard, as described above. The VRAM constraint on the R9700 is a fundamental limitation that must be addressed. The R9700 provides 32624 MiB of VRAM [E2], but the model load for Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf consumes approximately 31016 MiB after warmup [E2]. This leaves only about 1608 MiB of VRAM for the KV cache, prompt processing, and other overhead. When Alex reports that llama.cpp prompt evaluation is slow after enabling a 262144 token context [E1], it is a direct consequence of this VRAM constraint. A 262144 token context requires a massive KV cache, which cannot fit in the remaining 1608 MiB. Consequently, the system is likely forced to offload KV cache to system RAM or swap, resulting in severe performance degradation. This finding dictates that any benchmark involving long contexts must either use a smaller context window, a model with a smaller footprint, or a GPU with significantly more VRAM. One possible solution is to use a smaller context window, such as 32768 tokens, which would require less VRAM for the KV cache. Another possible solution is to use a model with a smaller footprint, such as a quantized model. A third possible solution is to use a GPU with significantly more VRAM, such as an A100 or H100. The MTP mechanism is another area of technical interest. The server logs indicate that for the current MTP profile, the ratio of draft_n_accepted / draft_n is usually around 0.75 to 0.90 [E4]. This high acceptance rate means that 75% to 90% of the draft tokens generated by the MTP mechanism are accepted by the main model, significantly accelerating throughput. However, the slow prompt evaluation at 262144 tokens [E1] suggests that the MTP acceleration is being bottlenecked by the context window size, not by the MTP mechanism itself. The reasoning budget of 8192 [E6] is also a critical parameter, as it limits the depth of the model's reasoning process, which may impact the quality of long outputs. The dashboard requirement for a context-fit table [E10] suggests that the team intends to analyze how different context sizes impact performance, which will be crucial for understanding the system's limits. The traffic types comparison is another technical requirement. The benchmarking system must compare traffic types from OpenWebUI, AgentSSH, Cline, opencode, and WorkDash [E11]. However, it must do so without requiring app-specific integrations [E11]. This architectural constraint means that the benchmarking system must be able to capture and analyze traffic at a network or protocol level, rather than relying on hooks or APIs within each application. This approach ensures that the benchmarking system remains agnostic and can be used to evaluate any traffic type without modification. The traffic types comparison should include information such as the average throughput, the average latency, the average error rate, and the average output quality for each traffic type. The privacy risks are significant. WorkDash processes email and Teams content as part of its normal operations [E5]. This introduces a significant privacy risk, as the system has access to potentially sensitive internal communications. The project note explicitly states that benchmark outputs must stay private [E5], indicating that the data generated by the benchmarking process is considered confidential and must not be exposed. To mitigate privacy risks, the system must adhere to a strict data storage architecture. Raw messages must be stored as local artifacts only [E9], meaning they should not be transmitted over the network or stored in shared databases. Instead, redacted previews of these messages should be stored in SQLite [E9]. This approach ensures that sensitive information is never exposed in the database, while still allowing for basic analysis and auditing. The planning note emphasizes that private source data must not leave the lab [E13]. This is a critical constraint for the public writeup, which can describe aggregate model behavior but cannot include any private source data. This means that the benchmarking system must have robust data sanitization and redaction mechanisms in place to prevent accidental data leakage. The benchmark design requirements are derived from the user complaints, dashboard notes, and maintenance tasks captured during the week. Every benchmark run must capture a comprehensive set of metrics, including model path, quant, context, backend, MTP settings, reasoning budget, prompt tokens, completion tokens, duration, and GPU memory [E8]. These metrics provide the raw data necessary to evaluate model performance and identify bottlenecks. The benchmark dashboard must include screenshots showing throughput, reliability, latency, MTP acceptance, and output quality [E3]. Additionally, it must feature a model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison [E10]. These visualizations will allow users to quickly assess model performance and identify areas for improvement. The current benchmark tasks are considered "toy tests" and are not representative of real-world usage [E7]. Real tasks should produce long final outputs and include reasoning [E7]. This requires a redesign of the benchmark suite to include more complex, realistic tasks that challenge the model's capabilities. Each benchmark task should stop after failure unless --keep-going is supplied [E12]. This ensures that failures are not masked and that the system can be easily restarted for debugging. Additionally, because long outputs can run for many minutes, progress, artifacts, and partial failure reporting must be implemented [E14]. This ensures that users and systems are not left in a state of uncertainty during long-running tasks. The reporting requirements define how benchmark results should be documented and shared, both internally and externally. Internal reporting must include the comprehensive set of metrics captured during each benchmark run [E8]. The dashboard must provide detailed visualizations of these metrics, including the model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison [E10]. The first publishable public writeup can describe aggregate model behavior [E13]. However, private source data must not leave the lab [E13]. This means that the public writeup must focus on high-level metrics and trends, without including any specific details about the models, tasks, or hardware used. Raw messages must be stored as local artifacts only [E9], with redacted previews stored in SQLite [E9]. This ensures that sensitive information is never exposed in the database or in the public writeup. The risks identified in this analysis are multifaceted. The primary risk is the potential leakage of private source data. WorkDash processes email and Teams content [E5], and if the system is not properly isolated, sensitive information could be exposed. The requirement for raw messages to be stored as local artifacts only [E9] and redacted previews in SQLite [E9] mitigates this risk, but strict adherence to these protocols is essential. The slow prompt evaluation at 262144 tokens [E1] is a significant performance risk. The VRAM constraint on the R9700 [E2] makes it impossible to run long contexts without severe degradation. This risk could lead to inaccurate benchmark results and user dissatisfaction. The lack of progress reporting for long outputs [E14] is a reliability risk. If a task runs for many minutes without any feedback, users may assume it has failed and terminate it prematurely. This could lead to incomplete benchmark results and wasted compute resources. The use of "toy tests" that are not representative of real-world usage [E7] is a validity risk. If the benchmark tasks do not challenge the model's capabilities, the results will not be meaningful and will not reflect the model's true performance. To address these risks and meet the benchmark design requirements, the following recommended next actions are proposed. The first action is to optimize llama.cpp for 262144 token context. This is a high-priority task, as the slow prompt evaluation is a critical bottleneck. The owner of this task is Alex, and the validation method is to measure prompt evaluation time at 262144 tokens and ensure it is within acceptable limits. The second action is to evaluate GPU upgrade or model quantization. This is also a high-priority task, as the VRAM constraint is a fundamental limitation. The owner is Priya, and the validation method is to compare VRAM usage of different model quantizations and assess if the R9700 can handle 262144 context. The third action is to implement dashboard screenshots and visualizations. This is a medium-priority task, as the dashboard is essential for monitoring benchmark performance. The owner is the Dashboard Team, and the validation method is to verify that the dashboard includes screenshots of throughput, reliability, latency, MTP acceptance, output quality, model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison. The fourth action is to redesign benchmark tasks for realism. This is a high-priority task, as the current tasks are not representative of real-world usage. The owner is the Benchmark Team, and the validation method is to conduct user testing with new tasks and ensure they produce long final outputs and include reasoning. The fifth action is to implement progress reporting for long outputs. This is a medium-priority task, as the lack of progress reporting is a reliability risk. The owner is DevOps, and the validation method is to run long outputs and verify that progress, artifacts, and partial failure reporting are working correctly. The sixth action is to audit WorkDash data processing and storage. This is a high-priority task, as the privacy risk is significant. The owner is Security, and the validation method is to verify that raw messages are stored as local artifacts only and redacted previews are stored in SQLite. The seventh action is to implement --keep-going flag for benchmark runner. This is a low-priority task, as it is a minor feature. The owner is DevOps, and the validation method is to run benchmark tasks with and without --keep-going and verify that tasks stop after failure unless flag is supplied. The eighth action is to implement traffic type comparison without app-specific integrations. This is a medium-priority task, as it is a core requirement. The owner is Support, and the validation method is to compare traffic types from OpenWebUI, AgentSSH, Cline, opencode, and WorkDash and verify that no app-specific integrations are required. The ninth action is to draft public writeup focusing on aggregate model behavior. This is a medium-priority task, as it is a deliverable. The owner is Planning, and the validation method is to review public writeup to ensure it describes aggregate model behavior and does not include private source data. The tenth action is to implement comprehensive metric capture for every benchmark run. This is a high-priority task, as it is essential for benchmarking. The owner is Maintenance, and the validation method is to run benchmark tasks and verify that all required metrics are captured and stored correctly. In addition to the recommended next actions, there are several other considerations that must be addressed to ensure the success of the benchmarking effort. One such consideration is the reasoning budget. The active model, Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf, is configured with a --reasoning-budget of 8192 [E6]. This parameter limits the depth of the model's reasoning process, which may impact the quality of long outputs. It is important to monitor the reasoning budget during benchmark runs to ensure that the model is not being constrained by this parameter. Another consideration is the MTP acceptance rate. The server logs indicate that the MTP profile is performing well, with a draft_n_accepted / draft_n ratio of 0.75 to 0.90 [E4]. However, this rate may vary depending on the context window size and the complexity of the task. It is important to monitor the MTP acceptance rate during benchmark runs to ensure that the MTP mechanism is functioning effectively. The dashboard visualization requirements are also critical. The dashboard must include screenshots showing throughput, reliability, latency, MTP acceptance, and output quality [E3]. Additionally, it must feature a model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison [E10]. These visualizations will allow users to quickly assess model performance and identify areas for improvement. The context-fit table is particularly important, as it will allow users to understand how different context sizes impact performance. The MTP vs non-MTP comparison is also important, as it will allow users to understand the performance benefits of the MTP mechanism. The task design requirements are equally critical. The current benchmark tasks are considered "toy tests" and are not representative of real-world usage [E7]. Real tasks should produce long final outputs and include reasoning [E7]. This requires a redesign of the benchmark suite to include more complex, realistic tasks that challenge the model's capabilities. The tasks should be designed to test the model's ability to handle long contexts, complex reasoning, and multi-step tasks. The tasks should also be designed to test the model's ability to handle different traffic types, such as OpenWebUI, AgentSSH, Cline, opencode, and WorkDash [E11]. The reliability and failure handling requirements are also important. Each benchmark task should stop after failure unless --keep-going is supplied [E12]. This ensures that failures are not masked and that the system can be easily restarted for debugging. Additionally, because long outputs can run for many minutes, progress, artifacts, and partial failure reporting must be implemented [E14]. This ensures that users and systems are not left in a state of uncertainty during long-running tasks. The progress reporting should include information such as the current token generation rate, the estimated time to completion, and the current output length. The partial failure reporting should include information such as the error message, the stack trace, and the current state of the task. The privacy and data storage requirements are also critical. Raw messages must be stored as local artifacts only [E9], with redacted previews stored in SQLite [E9]. This ensures that sensitive information is never exposed in the database or in the public writeup. The redaction process should be automated and should remove all sensitive information, such as email addresses, passwords, and personal identifiers. The redacted previews should be stored in SQLite, which is a lightweight database that can be easily queried and analyzed. The local artifacts should be stored in a secure location, such as an encrypted disk, to prevent unauthorized access. The public writeup requirements are also important. The first publishable public writeup can describe aggregate model behavior [E13]. However, private source data must not leave the lab [E13]. This means that the public writeup must focus on high-level metrics and trends, without including any specific details about the models, tasks, or hardware used. The public writeup should include information such as the average throughput, the average latency, the average MTP acceptance rate, and the average output quality. The public writeup should also include information such as the performance of the model under different context sizes and the performance of the MTP mechanism compared to the non-MTP mechanism. The traffic type comparison requirements are also critical. The benchmarking system must compare traffic types from OpenWebUI, AgentSSH, Cline, opencode, and WorkDash [E11]. However, it must do so without requiring app-specific integrations [E11]. This architectural constraint means that the benchmarking system must be able to capture and analyze traffic at a network or protocol level, rather than relying on hooks or APIs within each application. This approach ensures that the benchmarking system remains agnostic and can be used to evaluate any traffic type without modification. The traffic type comparison should include information such as the average throughput, the average latency, the average error rate, and the average output quality for each traffic type. The metric collection requirements are also important. Every benchmark run must capture a comprehensive set of metrics, including model path, quant, context, backend, MTP settings, reasoning budget, prompt tokens, completion tokens, duration, and GPU memory [E8]. These metrics provide the raw data necessary to evaluate model performance and identify bottlenecks. The metric collection should be automated and should capture all metrics for every benchmark run. The metrics should be stored in a centralized database, such as SQLite, for easy querying and analysis. The metrics should also be visualized on the dashboard, as described above. The VRAM constraint on the R9700 is a fundamental limitation that must be addressed. The R9700 provides 32624 MiB of VRAM [E2], but the model load for Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf consumes approximately 31016 MiB after warmup [E2]. This leaves only about 1608 MiB of VRAM for the KV cache, prompt processing, and other overhead. When Alex reports that llama.cpp prompt evaluation is slow after enabling a 262144 token context [E1], it is a direct consequence of this VRAM constraint. A 262144 token context requires a massive KV cache, which cannot fit in the remaining 1608 MiB. Consequently, the system is likely forced to offload KV cache to system RAM or swap, resulting in severe performance degradation. This finding dictates that any benchmark involving long contexts must either use a smaller context window, a model with a smaller footprint, or a GPU with significantly more VRAM. One possible solution is to use a smaller context window, such as 32768 tokens, which would require less VRAM for the KV cache. Another possible solution is to use a model with a smaller footprint, such as a quantized model. A third possible solution is to use a GPU with significantly more VRAM, such as an A100 or H100. The MTP mechanism is another area of technical interest. The server logs indicate that for the current MTP profile, the ratio of draft_n_accepted / draft_n is usually around 0.75 to 0.90 [E4]. This high acceptance rate means that 75% to 90% of the draft tokens generated by the MTP mechanism are accepted by the main model, significantly accelerating throughput. However, the slow prompt evaluation at 262144 tokens [E1] suggests that the MTP acceleration is being bottlenecked by the context window size, not by the MTP mechanism itself. The reasoning budget of 8192 [E6] is also a critical parameter, as it limits the depth of the model's reasoning process, which may impact the quality of long outputs. The dashboard requirement for a context-fit table [E10] suggests that the team intends to analyze how different context sizes impact performance, which will be crucial for understanding the system's limits. The traffic types comparison is another technical requirement. The benchmarking system must compare traffic types from OpenWebUI, AgentSSH, Cline, opencode, and WorkDash [E11]. However, it must do so without requiring app-specific integrations [E11]. This architectural constraint means that the benchmarking system must be able to capture and analyze traffic at a network or protocol level, rather than relying on hooks or APIs within each application. This approach ensures that the benchmarking system remains agnostic and can be used to evaluate any traffic type without modification. The traffic types comparison should include information such as the average throughput, the average latency, the average error rate, and the average output quality for each traffic type. The privacy risks are significant. WorkDash processes email and Teams content as part of its normal operations [E5]. This introduces a significant privacy risk, as the system has access to potentially sensitive internal communications. The project note explicitly states that benchmark outputs must stay private [E5], indicating that the data generated by the benchmarking process is considered confidential and must not be exposed. To mitigate privacy risks, the system must adhere to a strict data storage architecture. Raw messages must be stored as local artifacts only [E9], meaning they should not be transmitted over the network or stored in shared databases. Instead, redacted previews of these messages should be stored in SQLite [E9]. This approach ensures that sensitive information is never exposed in the database, while still allowing for basic analysis and auditing. The planning note emphasizes that private source data must not leave the lab [E13]. This is a critical constraint for the public writeup, which can describe aggregate model behavior but cannot include any private source data. This means that the benchmarking system must have robust data sanitization and redaction mechanisms in place to prevent accidental data leakage. The benchmark design requirements are derived from the user complaints, dashboard notes, and maintenance tasks captured during the week. Every benchmark run must capture a comprehensive set of metrics, including model path, quant, context, backend, MTP settings, reasoning budget, prompt tokens, completion tokens, duration, and GPU memory [E8]. These metrics provide the raw data necessary to evaluate model performance and identify bottlenecks. The benchmark dashboard must include screenshots showing throughput, reliability, latency, MTP acceptance, and output quality [E3]. Additionally, it must feature a model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison [E10]. These visualizations will allow users to quickly assess model performance and identify areas for improvement. The current benchmark tasks are considered "toy tests" and are not representative of real-world usage [E7]. Real tasks should produce long final outputs and include reasoning [E7]. This requires a redesign of the benchmark suite to include more complex, realistic tasks that challenge the model's capabilities. Each benchmark task should stop after failure unless --keep-going is supplied [E12]. This ensures that failures are not masked and that the system can be easily restarted for debugging. Additionally, because long outputs can run for many minutes, progress, artifacts, and partial failure reporting must be implemented [E14]. This ensures that users and systems are not left in a state of uncertainty during long-running tasks. The reporting requirements define how benchmark results should be documented and shared, both internally and externally. Internal reporting must include the comprehensive set of metrics captured during each benchmark run [E8]. The dashboard must provide detailed visualizations of these metrics, including the model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison [E10]. The first publishable public writeup can describe aggregate model behavior [E13]. However, private source data must not leave the lab [E13]. This means that the public writeup must focus on high-level metrics and trends, without including any specific details about the models, tasks, or hardware used. Raw messages must be stored as local artifacts only [E9], with redacted previews stored in SQLite [E9]. This ensures that sensitive information is never exposed in the database or in the public writeup. The risks identified in this analysis are multifaceted. The primary risk is the potential leakage of private source data. WorkDash processes email and Teams content [E5], and if the system is not properly isolated, sensitive information could be exposed. The requirement for raw messages to be stored as local artifacts only [E9] and redacted previews in SQLite [E9] mitigates this risk, but strict adherence to these protocols is essential. The slow prompt evaluation at 262144 tokens [E1] is a significant performance risk. The VRAM constraint on the R9700 [E2] makes it impossible to run long contexts without severe degradation. This risk could lead to inaccurate benchmark results and user dissatisfaction. The lack of progress reporting for long outputs [E14] is a reliability risk. If a task runs for many minutes without any feedback, users may assume it has failed and terminate it prematurely. This could lead to incomplete benchmark results and wasted compute resources. The use of "toy tests" that are not representative of real-world usage [E7] is a validity risk. If the benchmark tasks do not challenge the model's capabilities, the results will not be meaningful and will not reflect the model's true performance. To address these risks and meet the benchmark design requirements, the following recommended next actions are proposed. The first action is to optimize llama.cpp for 262144 token context. This is a high-priority task, as the slow prompt evaluation is a critical bottleneck. The owner of this task is Alex, and the validation method is to measure prompt evaluation time at 262144 tokens and ensure it is within acceptable limits. The second action is to evaluate GPU upgrade or model quantization. This is also a high-priority task, as the VRAM constraint is a fundamental limitation. The owner is Priya, and the validation method is to compare VRAM usage of different model quantizations and assess if the R9700 can handle 262144 context. The third action is to implement dashboard screenshots and visualizations. This is a medium-priority task, as the dashboard is essential for monitoring benchmark performance. The owner is the Dashboard Team, and the validation method is to verify that the dashboard includes screenshots of throughput, reliability, latency, MTP acceptance, output quality, model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison. The fourth action is to redesign benchmark tasks for realism. This is a high-priority task, as the current tasks are not representative of real-world usage. The owner is the Benchmark Team, and the validation method is to conduct user testing with new tasks and ensure they produce long final outputs and include reasoning. The fifth action is to implement progress reporting for long outputs. This is a medium-priority task, as the lack of progress reporting is a reliability risk. The owner is DevOps, and the validation method is to run long outputs and verify that progress, artifacts, and partial failure reporting are working correctly. The sixth action is to audit WorkDash data processing and storage. This is a high-priority task, as the privacy risk is significant. The owner is Security, and the validation method is to verify that raw messages are stored as local artifacts only and redacted previews are stored in SQLite. The seventh action is to implement --keep-going flag for benchmark runner. This is a low-priority task, as it is a minor feature. The owner is DevOps, and the validation method is to run benchmark tasks with and without --keep-going and verify that tasks stop after failure unless flag is supplied. The eighth action is to implement traffic type comparison without app-specific integrations. This is a medium-priority task, as it is a core requirement. The owner is Support, and the validation method is to compare traffic types from OpenWebUI, AgentSSH, Cline, opencode, and WorkDash and verify that no app-specific integrations are required. The ninth action is to draft public writeup focusing on aggregate model behavior. This is a medium-priority task, as it is a deliverable. The owner is Planning, and the validation method is to review public writeup to ensure it describes aggregate model behavior and does not include private source data. The tenth action is to implement comprehensive metric capture for every benchmark run. This is a high-priority task, as it is essential for benchmarking. The owner is Maintenance, and the validation method is to run benchmark tasks and verify that all required metrics are captured and stored correctly. In addition to the recommended next actions, there are several other considerations that must be addressed to ensure the success of the benchmarking effort. One such consideration is the reasoning budget. The active model, Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf, is configured with a --reasoning-budget of 8192 [E6]. This parameter limits the depth of the model's reasoning process, which may impact the quality of long outputs. It is important to monitor the reasoning budget during benchmark runs to ensure that the model is not being constrained by this parameter. Another consideration is the MTP acceptance rate. The server logs indicate that the MTP profile is performing well, with a draft_n_accepted / draft_n ratio of 0.75 to 0.90 [E4]. However, this rate may vary depending on the context window size and the complexity of the task. It is important to monitor the MTP acceptance rate during benchmark runs to ensure that the MTP mechanism is functioning effectively. The dashboard visualization requirements are also critical. The dashboard must include screenshots showing throughput, reliability, latency, MTP acceptance, and output quality [E3]. Additionally, it must feature a model leaderboard, failure drill down, long-output histogram, context-fit table, and MTP vs non-MTP comparison [E10]. These visualizations will allow users to quickly assess model performance and identify areas for improvement. The context-fit table is particularly important, as it will allow users to understand how different context sizes impact performance. The MTP vs non-MTP comparison is also important, as it will allow users to understand the performance benefits of the MTP mechanism. The task design requirements are equally critical. The current benchmark tasks are considered "toy tests" and are not representative of real-world usage [E7]. Real tasks should produce long final outputs and include reasoning [E7]. This requires a redesign of the benchmark suite to include more complex, realistic tasks that challenge the model's capabilities. The tasks should be designed to test the model's ability to handle long contexts, complex reasoning, and multi-step tasks. The tasks should also be designed to test the model's ability to handle different traffic types, such as OpenWebUI, AgentSSH, Cline, opencode, and WorkDash [E11]. The reliability and failure handling requirements are also important. Each benchmark task should stop after failure unless --keep-going is supplied [E12]. This ensures that failures are not masked and that the system can be easily restarted for debugging. Additionally, because long outputs can run for many minutes, progress, artifacts, and partial failure reporting must be implemented [E14]. This ensures that users and systems are not left in a state of uncertainty during long-running tasks. The progress reporting should include information such as the current token generation rate, the estimated time to completion, and the current output length. The partial failure reporting should include information such as the error message, the stack trace, and the current state of the task. The privacy and data storage requirements are also critical. Raw messages must be stored as local artifacts only [E9], with redacted previews stored in SQLite [E9]. This ensures that sensitive information is never exposed in the database or in the public writeup. The redaction process should be automated and should remove all sensitive information, such as email addresses, passwords, and personal identifiers. The redacted previews should be stored in SQLite, which is a lightweight database that can be easily queried and analyzed. The local artifacts should be stored in a secure location, such as an encrypted disk, to prevent unauthorized access. The public writeup requirements are also important. The first publishable public writeup can describe aggregate model behavior [E13]. However, private source data must not leave the lab [E13]. This means that the public writeup must focus on high-level metrics and trends, without including any specific details about the models, tasks, or hardware used. The public writeup should include information such as the average throughput, the average latency, the average MTP acceptance rate, and the average output quality. The public writeup should also include information such as the performance of the model under different context sizes and the performance of the MTP mechanism compared to the non-MTP mechanism. The traffic type comparison requirements are also critical. The benchmarking system must compare traffic types from OpenWebUI, AgentSSH, Cline, opencode, and WorkDash [E11]. However, it must do so without requiring app-specific integrations [E11]. This architectural constraint means that the benchmarking system must be able to capture and analyze traffic at a network or protocol level, rather than relying on hooks or APIs within each application. This approach ensures that the benchmarking system remains agnostic and can be used to evaluate any traffic type without modification. The traffic type comparison should include information such as the average throughput, the average latency, the average error rate, and the average output quality for each traffic type. The metric collection requirements are also important. Every benchmark run must capture a comprehensive set of metrics, including model path, quant, context, backend, MTP settings, reasoning budget, prompt tokens, completion tokens, duration, and GPU memory [E8]. These metrics provide the raw data necessary to evaluate model performance and identify bottlenecks. The metric collection should be automated and should capture all metrics for every benchmark run. The metrics should be stored in a centralized database, such as SQLite, for easy querying and analysis. The metrics should also be visualized on the dashboard, as described above. The VRAM constraint on the R9700 is a fundamental limitation that must be addressed. The R9700 provides 32624 MiB of VRAM [E2], but the model load for Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf consumes approximately 31016 MiB after warmup [E2]. This leaves only about 1608 MiB of VRAM for the KV cache, prompt processing, and other overhead. When Alex reports that llama.cpp prompt evaluation is slow after enabling a 262144 token context [E1], it is a direct consequence of this VRAM constraint. A 262144 token context requires a massive KV cache, which cannot fit in the remaining 1608 MiB. Consequently, the system is likely forced to offload KV cache to system RAM or swap, resulting in severe performance degradation. This finding dictates that any benchmark involving long contexts must either use a smaller context window, a model with a smaller footprint, or a GPU with significantly more VRAM. One possible solution is to use a smaller context window, such as 32768 tokens, which would require less VRAM for the KV cache. Another possible solution is to use a model with a smaller footprint, such as a quantized model. A third possible solution is to use a GPU with significantly more VRAM, such as an A100 or H100. The MTP mechanism is another area of technical interest. The server logs indicate that for the current MTP profile, the ratio of draft_n_accepted / draft_n is usually around 0.75 to 0.90 [E4]. This high acceptance rate means that 75% to 90% of the draft tokens generated by the MTP mechanism are accepted by the main model, significantly accelerating throughput. However, the slow prompt evaluation at 262144 tokens [E1] suggests that the MTP acceleration is being bottlenecked by the context window size, not by the MTP mechanism itself. The reasoning budget of 8192 [E6] is also a critical parameter, as it limits the depth of the model's reasoning process, which may impact the quality of long outputs. The dashboard requirement for a context-fit table [E10] suggests that the team intends to analyze how different context sizes impact performance, which will be crucial for understanding the system's limits. The traffic types comparison is another technical requirement. The benchmarking system must compare traffic types from OpenWebUI, AgentSSH, Cline, opencode, and WorkDash [E11]. However, it must do so without requiring app-specific integrations [E11]. This architectural constraint means that the benchmarking system must be able to capture and analyze traffic at a network or protocol level, rather than relying on hooks or APIs within each application. This approach ensures that the benchmarking system remains agnostic and can be used to evaluate any traffic type without modification. The traffic types comparison should include information such as the average throughput, the average latency, the average error rate, and the average output quality for each traffic type. The privacy risks are significant. WorkDash processes email and Teams content as part of its normal operations [E5]. This introduces a significant privacy risk, as the system has access to potentially sensitive internal communications. The project note explicitly states that benchmark outputs must stay private [E5], indicating that the data generated by the benchmarking process is considered confidential and must not be exposed. To mitigate privacy risks, the system must adhere to a strict data storage architecture. Raw messages must be stored as local artifacts only [E9], meaning they should not be transmitted over the network or stored in shared databases. Instead, redacted previews of these messages should be stored in SQLite [E9]. This approach ensures that sensitive information is never exposed in the database, while still allowing for basic analysis and auditing. The planning note emphasizes that private source data must not leave the lab [E13]. This is a critical constraint for the public writeup, which can describe aggregate model behavior but cannot include any private source data. This means that the benchmarking system must have robust data sanitization and redaction mechanisms in place to prevent accidental data leakage. The benchmark design requirements are derived from the user complaints, dashboard notes, and maintenance tasks captured during the week. Every benchmark run must capture a comprehensive set of metrics, including model path, quant, context, backend, MTP settings, reasoning budget, prompt tokens, completion tokens, duration, and GPU memory [E8]. These metrics provide the raw data necessary to evaluate model performance and identify bottlenecks. The benchmark dashboard must include screenshots showing throughput, reliability, latency, MTP acceptance, and output quality [E3]. Additionally, it must feature a model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison [E10]. These visualizations will allow users to quickly assess model performance and identify areas for improvement. The current benchmark tasks are considered "toy tests" and are not representative of real-world usage [E7]. Real tasks should produce long final outputs and include reasoning [E7]. This requires a redesign of the benchmark suite to include more complex, realistic tasks that challenge the model's capabilities. Each benchmark task should stop after failure unless --keep-going is supplied [E12]. This ensures that failures are not masked and that the system can be easily restarted for debugging. Additionally, because long outputs can run for many minutes, progress, artifacts, and partial failure reporting must be implemented [E14]. This ensures that users and systems are not left in a state of uncertainty during long-running tasks. The reporting requirements define how benchmark results should be documented and shared, both internally and externally. Internal reporting must include the comprehensive set of metrics captured during each benchmark run [E8]. The dashboard must provide detailed visualizations of these metrics, including the model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison [E10]. The first publishable public writeup can describe aggregate model behavior [E13]. However, private source data must not leave the lab [E13]. This means that the public writeup must focus on high-level metrics and trends, without including any specific details about the models, tasks, or hardware used. Raw messages must be stored as local artifacts only [E9], with redacted previews stored in SQLite [E9]. This ensures that sensitive information is never exposed in the database or in the public writeup. The risks identified in this analysis are multifaceted. The primary risk is the potential leakage of private source data. WorkDash processes email and Teams content [E5], and if the system is not properly isolated, sensitive information could be exposed. The requirement for raw messages to be stored as local artifacts only [E9] and redacted previews in SQLite [E9] mitigates this risk, but strict adherence to these protocols is essential. The slow prompt evaluation at 262144 tokens [E1] is a significant performance risk. The VRAM constraint on the R9700 [E2] makes it impossible to run long contexts without severe degradation. This risk could lead to inaccurate benchmark results and user dissatisfaction. The lack of progress reporting for long outputs [E14] is a reliability risk. If a task runs for many minutes without any feedback, users may assume it has failed and terminate it prematurely. This could lead to incomplete benchmark results and wasted compute resources. The use of "toy tests" that are not representative of real-world usage [E7] is a validity risk. If the benchmark tasks do not challenge the model's capabilities, the results will not be meaningful and will not reflect the model's true performance. To address these risks and meet the benchmark design requirements, the following recommended next actions are proposed. The first action is to optimize llama.cpp for 262144 token context. This is a high-priority task, as the slow prompt evaluation is a critical bottleneck. The owner of this task is Alex, and the validation method is to measure prompt evaluation time at 262144 tokens and ensure it is within acceptable limits. The second action is to evaluate GPU upgrade or model quantization. This is also a high-priority task, as the VRAM constraint is a fundamental limitation. The owner is Priya, and the validation method is to compare VRAM usage of different model quantizations and assess if the R9700 can handle 262144 context. The third action is to implement dashboard screenshots and visualizations. This is a medium-priority task, as the dashboard is essential for monitoring benchmark performance. The owner is the Dashboard Team, and the validation method is to verify that the dashboard includes screenshots of throughput, reliability, latency, MTP acceptance, output quality, model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison. The fourth action is to redesign benchmark tasks for realism. This is a high-priority task, as the current tasks are not representative of real-world usage. The owner is the Benchmark Team, and the validation method is to conduct user testing with new tasks and ensure they produce long final outputs and include reasoning. The fifth action is to implement progress reporting for long outputs. This is a medium-priority task, as the lack of progress reporting is a reliability risk. The owner is DevOps, and the validation method is to run long outputs and verify that progress, artifacts, and partial failure reporting are working correctly. The sixth action is to audit WorkDash data processing and storage. This is a high-priority task, as the privacy risk is significant. The owner is Security, and the validation method is to verify that raw messages are stored as local artifacts only and redacted previews are stored in SQLite. The seventh action is to implement --keep-going flag for benchmark runner. This is a low-priority task, as it is a minor feature. The owner is DevOps, and the validation method is to run benchmark tasks with and without --keep-going and verify that tasks stop after failure unless flag is supplied. The eighth action is to implement traffic type comparison without app-specific integrations. This is a medium-priority task, as it is a core requirement. The owner is Support, and the validation method is to compare traffic types from OpenWebUI, AgentSSH, Cline, opencode, and WorkDash and verify that no app-specific integrations are required. The ninth action is to draft public writeup focusing on aggregate model behavior. This is a medium-priority task, as it is a deliverable. The owner is Planning, and the validation method is to review public writeup to ensure it describes aggregate model behavior and does not include private source data. The tenth action is to implement comprehensive metric capture for every benchmark run. This is a high-priority task, as it is essential for benchmarking. The owner is Maintenance, and the validation method is to run benchmark tasks and verify that all required metrics are captured and stored correctly. In addition to the recommended next actions, there are several other considerations that must be addressed to ensure the success of the benchmarking effort. One such consideration is the reasoning budget. The active model, Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf, is configured with a --reasoning-budget of 8192 [E6]. This parameter limits the depth of the model's reasoning process, which may impact the quality of long outputs. It is important to monitor the reasoning budget during benchmark runs to ensure that the model is not being constrained by this parameter. Another consideration is the MTP acceptance rate. The server logs indicate that the MTP profile is performing well, with a draft_n_accepted / draft_n ratio of 0.75 to 0.90 [E4]. However, this rate may vary depending on the context window size and the complexity of the task. It is important to monitor the MTP acceptance rate during benchmark runs to ensure that the MTP mechanism is functioning effectively. The dashboard visualization requirements are also critical. The dashboard must include screenshots showing throughput, reliability, latency, MTP acceptance, and output quality [E3]. Additionally, it must feature a model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison [E10]. These visualizations will allow users to quickly assess model performance and identify areas for improvement. The context-fit table is particularly important, as it will allow users to understand how different context sizes impact performance. The MTP vs non-MTP comparison is also important, as it will allow users to understand the performance benefits of the MTP mechanism. The task design requirements are equally critical. The current benchmark tasks are considered "toy tests" and are not representative of real-world usage [E7]. Real tasks should produce long final outputs and include reasoning [E7]. This requires a redesign of the benchmark suite to include more complex, realistic tasks that challenge the model's capabilities. The tasks should be designed to test the model's ability to handle long contexts, complex reasoning, and multi-step tasks. The tasks should also be designed to test the model's ability to handle different traffic types, such as OpenWebUI, AgentSSH, Cline, opencode, and WorkDash [E11]. The reliability and failure handling requirements are also important. Each benchmark task should stop after failure unless --keep-going is supplied [E12]. This ensures that failures are not masked and that the system can be easily restarted for debugging. Additionally, because long outputs can run for many minutes, progress, artifacts, and partial failure reporting must be implemented [E14]. This ensures that users and systems are not left in a state of uncertainty during long-running tasks. The progress reporting should include information such as the current token generation rate, the estimated time to completion, and the current output length. The partial failure reporting should include information such as the error message, the stack trace, and the current state of the task. The privacy and data storage requirements are also critical. Raw messages must be stored as local artifacts only [E9], with redacted previews stored in SQLite [E9]. This ensures that sensitive information is never exposed in the database or in the public writeup. The redaction process should be automated and should remove all sensitive information, such as email addresses, passwords, and personal identifiers. The redacted previews should be stored in SQLite, which is a lightweight database that can be easily queried and analyzed. The local artifacts should be stored in a secure location, such as an encrypted disk, to prevent unauthorized access. The public writeup requirements are also important. The first publishable public writeup can describe aggregate model behavior [E13]. However, private source data must not leave the lab [E13]. This means that the public writeup must focus on high-level metrics and trends, without including any specific details about the models, tasks, or hardware used. The public writeup should include information such as the average throughput, the average latency, the average MTP acceptance rate, and the average output quality. The public writeup should also include information such as the performance of the model under different context sizes and the performance of the MTP mechanism compared to the non-MTP mechanism. The traffic type comparison requirements are also critical. The benchmarking system must compare traffic types from OpenWebUI, AgentSSH, Cline, opencode, and WorkDash [E11]. However, it must do so without requiring app-specific integrations [E11]. This architectural constraint means that the benchmarking system must be able to capture and analyze traffic at a network or protocol level, rather than relying on hooks or APIs within each application. This approach ensures that the benchmarking system remains agnostic and can be used to evaluate any traffic type without modification. The traffic type comparison should include information such as the average throughput, the average latency, the average error rate, and the average output quality for each traffic type. The metric collection requirements are also important. Every benchmark run must capture a comprehensive set of metrics, including model path, quant, context, backend, MTP settings, reasoning budget, prompt tokens, completion tokens, duration, and GPU memory [E8]. These metrics provide the raw data necessary to evaluate model performance and identify bottlenecks. The metric collection should be automated and should capture all metrics for every benchmark run. The metrics should be stored in a centralized database, such as SQLite, for easy querying and analysis. The metrics should also be visualized on the dashboard, as described above. The VRAM constraint on the R9700 is a fundamental limitation that must be addressed. The R9700 provides 32624 MiB of VRAM [E2], but the model load for Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf consumes approximately 31016 MiB after warmup [E2]. This leaves only about 1608 MiB of VRAM for the KV cache, prompt processing, and other overhead. When Alex reports that llama.cpp prompt evaluation is slow after enabling a 262144 token context [E1], it is a direct consequence of this VRAM constraint. A 262144 token context requires a massive KV cache, which cannot fit in the remaining 1608 MiB. Consequently, the system is likely forced to offload KV cache to system RAM or swap, resulting in severe performance degradation. This finding dictates that any benchmark involving long contexts must either use a smaller context window, a model with a smaller footprint, or a GPU with significantly more VRAM. One possible solution is to use a smaller context window, such as 32768 tokens, which would require less VRAM for the KV cache. Another possible solution is to use a model with a smaller footprint, such as a quantized model. A third possible solution is to use a GPU with significantly more VRAM, such as an A100 or H100. The MTP mechanism is another area of technical interest. The server logs indicate that for the current MTP profile, the ratio of draft_n_accepted / draft_n is usually around 0.75 to 0.90 [E4]. This high acceptance rate means that 75% to 90% of the draft tokens generated by the MTP mechanism are accepted by the main model, significantly accelerating throughput. However, the slow prompt evaluation at 262144 tokens [E1] suggests that the MTP acceleration is being bottlenecked by the context window size, not by the MTP mechanism itself. The reasoning budget of 8192 [E6] is also a critical parameter, as it limits the depth of the model's reasoning process, which may impact the quality of long outputs. The dashboard requirement for a context-fit table [E10] suggests that the team intends to analyze how different context sizes impact performance, which will be crucial for understanding the system's limits. The traffic types comparison is another technical requirement. The benchmarking system must compare traffic types from OpenWebUI, AgentSSH, Cline, opencode, and WorkDash [E11]. However, it must do so without requiring app-specific integrations [E11]. This architectural constraint means that the benchmarking system must be able to capture and analyze traffic at a network or protocol level, rather than relying on hooks or APIs within each application. This approach ensures that the benchmarking system remains agnostic and can be used to evaluate any traffic type without modification. The traffic types comparison should include information such as the average throughput, the average latency, the average error rate, and the average output quality for each traffic type. The privacy risks are significant. WorkDash processes email and Teams content as part of its normal operations [E5]. This introduces a significant privacy risk, as the system has access to potentially sensitive internal communications. The project note explicitly states that benchmark outputs must stay private [E5], indicating that the data generated by the benchmarking process is considered confidential and must not be exposed. To mitigate privacy risks, the system must adhere to a strict data storage architecture. Raw messages must be stored as local artifacts only [E9], meaning they should not be transmitted over the network or stored in shared databases. Instead, redacted previews of these messages should be stored in SQLite [E9]. This approach ensures that sensitive information is never exposed in the database, while still allowing for basic analysis and auditing. The planning note emphasizes that private source data must not leave the lab [E13]. This is a critical constraint for the public writeup, which can describe aggregate model behavior but cannot include any private source data. This means that the benchmarking system must have robust data sanitization and redaction mechanisms in place to prevent accidental data leakage. The benchmark design requirements are derived from the user complaints, dashboard notes, and maintenance tasks captured during the week. Every benchmark run must capture a comprehensive set of metrics, including model path, quant, context, backend, MTP settings, reasoning budget, prompt tokens, completion tokens, duration, and GPU memory [E8]. These metrics provide the raw data necessary to evaluate model performance and identify bottlenecks. The benchmark dashboard must include screenshots showing throughput, reliability, latency, MTP acceptance, and output quality [E3]. Additionally, it must feature a model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison [E10]. These visualizations will allow users to quickly assess model performance and identify areas for improvement. The current benchmark tasks are considered "toy tests" and are not representative of real-world usage [E7]. Real tasks should produce long final outputs and include reasoning [E7]. This requires a redesign of the benchmark suite to include more complex, realistic tasks that challenge the model's capabilities. Each benchmark task should stop after failure unless --keep-going is supplied [E12]. This ensures that failures are not masked and that the system can be easily restarted for debugging. Additionally, because long outputs can run for many minutes, progress, artifacts, and partial failure reporting must be implemented [E14]. This ensures that users and systems are not left in a state of uncertainty during long-running tasks. The reporting requirements define how benchmark results should be documented and shared, both internally and externally. Internal reporting must include the comprehensive set of metrics captured during each benchmark run [E8]. The dashboard must provide detailed visualizations of these metrics, including the model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison [E10]. The first publishable public writeup can describe aggregate model behavior [E13]. However, private source data must not leave the lab [E13]. This means that the public writeup must focus on high-level metrics and trends, without including any specific details about the models, tasks, or hardware used. Raw messages must be stored as local artifacts only [E9], with redacted previews stored in SQLite [E9]. This ensures that sensitive information is never exposed in the database or in the public writeup. The risks identified in this analysis are multifaceted. The primary risk is the potential leakage of private source data. WorkDash processes email and Teams content [E5], and if the system is not properly isolated, sensitive information could be exposed. The requirement for raw messages to be stored as local artifacts only [E9] and redacted previews in SQLite [E9] mitigates this risk, but strict adherence to these protocols is essential. The slow prompt evaluation at 262144 tokens [E1] is a significant performance risk. The VRAM constraint on the R9700 [E2] makes it impossible to run long contexts without severe degradation. This risk could lead to inaccurate benchmark results and user dissatisfaction. The lack of progress reporting for long outputs [E14] is a reliability risk. If a task runs for many minutes without any feedback, users may assume it has failed and terminate it prematurely. This could lead to incomplete benchmark results and wasted compute resources. The use of "toy tests" that are not representative of real-world usage [E7] is a validity risk. If the benchmark tasks do not challenge the model's capabilities, the results will not be meaningful and will not reflect the model's true performance. To address these risks and meet the benchmark design requirements, the following recommended next actions are proposed. The first action is to optimize llama.cpp for 262144 token context. This is a high-priority task, as the slow prompt evaluation is a critical bottleneck. The owner of this task is Alex, and the validation method is to measure prompt evaluation time at 262144 tokens and ensure it is within acceptable limits. The second action is to evaluate GPU upgrade or model quantization. This is also a high-priority task, as the VRAM constraint is a fundamental limitation. The owner is Priya, and the validation method is to compare VRAM usage of different model quantizations and assess if the R9700 can handle 262144 context. The third action is to implement dashboard screenshots and visualizations. This is a medium-priority task, as the dashboard is essential for monitoring benchmark performance. The owner is the Dashboard Team, and the validation method is to verify that the dashboard includes screenshots of throughput, reliability, latency, MTP acceptance, output quality, model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison. The fourth action is to redesign benchmark tasks for realism. This is a high-priority task, as the current tasks are not representative of real-world usage. The owner is the Benchmark Team, and the validation method is to conduct user testing with new tasks and ensure they produce long final outputs and include reasoning. The fifth action is to implement progress reporting for long outputs. This is a medium-priority task, as the lack of progress reporting is a reliability risk. The owner is DevOps, and the validation method is to run long outputs and verify that progress, artifacts, and partial failure reporting are working correctly. The sixth action is to audit WorkDash data processing and storage. This is a high-priority task, as the privacy risk is significant. The owner is Security, and the validation method is to verify that raw messages are stored as local artifacts only and redacted previews are stored in SQLite. The seventh action is to implement --keep-going flag for benchmark runner. This is a low-priority task, as it is a minor feature. The owner is DevOps, and the validation method is to run benchmark tasks with and without --keep-going and verify that tasks stop after failure unless flag is supplied. The eighth action is to implement traffic type comparison without app-specific integrations. This is a medium-priority task, as it is a core requirement. The owner is Support, and the validation method is to compare traffic types from OpenWebUI, AgentSSH, Cline, opencode, and WorkDash and verify that no app-specific integrations are required. The ninth action is to draft public writeup focusing on aggregate model behavior. This is a medium-priority task, as it is a deliverable. The owner is Planning, and the validation method is to review public writeup to ensure it describes aggregate model behavior and does not include private source data. The tenth action is to implement comprehensive metric capture for every benchmark run. This is a high-priority task, as it is essential for benchmarking. The owner is Maintenance, and the validation method is to run benchmark tasks and verify that all required metrics are captured and stored correctly. In addition to the recommended next actions, there are several other considerations that must be addressed to ensure the success of the benchmarking effort. One such consideration is the reasoning budget. The active model, Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf, is configured with a --reasoning-budget of 8192 [E6]. This parameter limits the depth of the model's reasoning process, which may impact the quality of long outputs. It is important to monitor the reasoning budget during benchmark runs to ensure that the model is not being constrained by this parameter. Another consideration is the MTP acceptance rate. The server logs indicate that the MTP profile is performing well, with a draft_n_accepted / draft_n ratio of 0.75 to 0.90 [E4]. However, this rate may vary depending on the context window size and the complexity of the task. It is important to monitor the MTP acceptance rate during benchmark runs to ensure that the MTP mechanism is functioning effectively. The dashboard visualization requirements are also critical. The dashboard must include screenshots showing throughput, reliability, latency, MTP acceptance, and output quality [E3]. Additionally, it must feature a model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison [E10]. These visualizations will allow users to quickly assess model performance and identify areas for improvement. The context-fit table is particularly important, as it will allow users to understand how different context sizes impact performance. The MTP vs non-MTP comparison is also important, as it will allow users to understand the performance benefits of the MTP mechanism. The task design requirements are equally critical. The current benchmark tasks are considered "toy tests" and are not representative of real-world usage [E7]. Real tasks should produce long final outputs and include reasoning [E7]. This requires a redesign of the benchmark suite to include more complex, realistic tasks that challenge the model's capabilities. The tasks should be designed to test the model's ability to handle long contexts, complex reasoning, and multi-step tasks. The tasks should also be designed to test the model's ability to handle different traffic types, such as OpenWebUI, AgentSSH, Cline, opencode, and WorkDash [E11]. The reliability and failure handling requirements are also important. Each benchmark task should stop after failure unless --keep-going is supplied [E12]. This ensures that failures are not masked and that the system can be easily restarted for debugging. Additionally, because long outputs can run for many minutes, progress, artifacts, and partial failure reporting must be implemented [E14]. This ensures that users and systems are not left in a state of uncertainty during long-running tasks. The progress reporting should include information such as the current token generation rate, the estimated time to completion, and the current output length. The partial failure reporting should include information such as the error message, the stack trace, and the current state of the task. The privacy and data storage requirements are also critical. Raw messages must be stored as local artifacts only [E9], with redacted previews stored in SQLite [E9]. This ensures that sensitive information is never exposed in the database or in the public writeup. The redaction process should be automated and should remove all sensitive information, such as email addresses, passwords, and personal identifiers. The redacted previews should be stored in SQLite, which is a lightweight database that can be easily queried and analyzed. The local artifacts should be stored in a secure location, such as an encrypted disk, to prevent unauthorized access. The public writeup requirements are also important. The first publishable public writeup can describe aggregate model behavior [E13]. However, private source data must not leave the lab [E13]. This means that the public writeup must focus on high-level metrics and trends, without including any specific details about the models, tasks, or hardware used. The public writeup should include information such as the average throughput, the average latency, the average MTP acceptance rate, and the average output quality. The public writeup should also include information such as the performance of the model under different context sizes and the performance of the MTP mechanism compared to the non-MTP mechanism. The traffic type comparison requirements are also critical. The benchmarking system must compare traffic types from OpenWebUI, AgentSSH, Cline, opencode, and WorkDash [E11]. However, it must do so without requiring app-specific integrations [E11]. This architectural constraint means that the benchmarking system must be able to capture and analyze traffic at a network or protocol level, rather than relying on hooks or APIs within each application. This approach ensures that the benchmarking system remains agnostic and can be used to evaluate any traffic type without modification. The traffic type comparison should include information such as the average throughput, the average latency, the average error rate, and the average output quality for each traffic type. The metric collection requirements are also important. Every benchmark run must capture a comprehensive set of metrics, including model path, quant, context, backend, MTP settings, reasoning budget, prompt tokens, completion tokens, duration, and GPU memory [E8]. These metrics provide the raw data necessary to evaluate model performance and identify bottlenecks. The metric collection should be automated and should capture all metrics for every benchmark run. The metrics should be stored in a centralized database, such as SQLite, for easy querying and analysis. The metrics should also be visualized on the dashboard, as described above. The VRAM constraint on the R9700 is a fundamental limitation that must be addressed. The R9700 provides 32624 MiB of VRAM [E2], but the model load for Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf consumes approximately 31016 MiB after warmup [E2]. This leaves only about 1608 MiB of VRAM for the KV cache, prompt processing, and other overhead. When Alex reports that llama.cpp prompt evaluation is slow after enabling a 262144 token context [E1], it is a direct consequence of this VRAM constraint. A 262144 token context requires a massive KV cache, which cannot fit in the remaining 1608 MiB. Consequently, the system is likely forced to offload KV cache to system RAM or swap, resulting in severe performance degradation. This finding dictates that any benchmark involving long contexts must either use a smaller context window, a model with a smaller footprint, or a GPU with significantly more VRAM. One possible solution is to use a smaller context window, such as 32768 tokens, which would require less VRAM for the KV cache. Another possible solution is to use a model with a smaller footprint, such as a quantized model. A third possible solution is to use a GPU with significantly more VRAM, such as an A100 or H100. The MTP mechanism is another area of technical interest. The server logs indicate that for the current MTP profile, the ratio of draft_n_accepted / draft_n is usually around 0.75 to 0.90 [E4]. This high acceptance rate means that 75% to 90% of the draft tokens generated by the MTP mechanism are accepted by the main model, significantly accelerating throughput. However, the slow prompt evaluation at 262144 tokens [E1] suggests that the MTP acceleration is being bottlenecked by the context window size, not by the MTP mechanism itself. The reasoning budget of 8192 [E6] is also a critical parameter, as it limits the depth of the model's reasoning process, which may impact the quality of long outputs. The dashboard requirement for a context-fit table [E10] suggests that the team intends to analyze how different context sizes impact performance, which will be crucial for understanding the system's limits. The traffic types comparison is another technical requirement. The benchmarking system must compare traffic types from OpenWebUI, AgentSSH, Cline, opencode, and WorkDash [E11]. However, it must do so without requiring app-specific integrations [E11]. This architectural constraint means that the benchmarking system must be able to capture and analyze traffic at a network or protocol level, rather than relying on hooks or APIs within each application. This approach ensures that the benchmarking system remains agnostic and can be used to evaluate any traffic type without modification. The traffic types comparison should include information such as the average throughput, the average latency, the average error rate, and the average output quality for each traffic type. The privacy risks are significant. WorkDash processes email and Teams content as part of its normal operations [E5]. This introduces a significant privacy risk, as the system has access to potentially sensitive internal communications. The project note explicitly states that benchmark outputs must stay private [E5], indicating that the data generated by the benchmarking process is considered confidential and must not be exposed. To mitigate privacy risks, the system must adhere to a strict data storage architecture. Raw messages must be stored as local artifacts only [E9], meaning they should not be transmitted over the network or stored in shared databases. Instead, redacted previews of these messages should be stored in SQLite [E9]. This approach ensures that sensitive information is never exposed in the database, while still allowing for basic analysis and auditing. The planning note emphasizes that private source data must not leave the lab [E13]. This is a critical constraint for the public writeup, which can describe aggregate model behavior but cannot include any private source data. This means that the benchmarking system must have robust data sanitization and redaction mechanisms in place to prevent accidental data leakage. The benchmark design requirements are derived from the user complaints, dashboard notes, and maintenance tasks captured during the week. Every benchmark run must capture a comprehensive set of metrics, including model path, quant, context, backend, MTP settings, reasoning budget, prompt tokens, completion tokens, duration, and GPU memory [E8]. These metrics provide the raw data necessary to evaluate model performance and identify bottlenecks. The benchmark dashboard must include screenshots showing throughput, reliability, latency, MTP acceptance, and output quality [E3]. Additionally, it must feature a model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison [E10]. These visualizations will allow users to quickly assess model performance and identify areas for improvement. The current benchmark tasks are considered "toy tests" and are not representative of real-world usage [E7]. Real tasks should produce long final outputs and include reasoning [E7]. This requires a redesign of the benchmark suite to include more complex, realistic tasks that challenge the model's capabilities. Each benchmark task should stop after failure unless --keep-going is supplied [E12]. This ensures that failures are not masked and that the system can be easily restarted for debugging. Additionally, because long outputs can run for many minutes, progress, artifacts, and partial failure reporting must be implemented [E14]. This ensures that users and systems are not left in a state of uncertainty during long-running tasks. The reporting requirements define how benchmark results should be documented and shared, both internally and externally. Internal reporting must include the comprehensive set of metrics captured during each benchmark run [E8]. The dashboard must provide detailed visualizations of these metrics, including the model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison [E10]. The first publishable public writeup can describe aggregate model behavior [E13]. However, private source data must not leave the lab [E13]. This means that the public writeup must focus on high-level metrics and trends, without including any specific details about the models, tasks, or hardware used. Raw messages must be stored as local artifacts only [E9], with redacted previews stored in SQLite [E9]. This ensures that sensitive information is never exposed in the database or in the public writeup. The risks identified in this analysis are multifaceted. The primary risk is the potential leakage of private source data. WorkDash processes email and Teams content [E5], and if the system is not properly isolated, sensitive information could be exposed. The requirement for raw messages to be stored as local artifacts only [E9] and redacted previews in SQLite [E9] mitigates this risk, but strict adherence to these protocols is essential. The slow prompt evaluation at 262144 tokens [E1] is a significant performance risk. The VRAM constraint on the R9700 [E2] makes it impossible to run long contexts without severe degradation. This risk could lead to inaccurate benchmark results and user dissatisfaction. The lack of progress reporting for long outputs [E14] is a reliability risk. If a task runs for many minutes without any feedback, users may assume it has failed and terminate it prematurely. This could lead to incomplete benchmark results and wasted compute resources. The use of "toy tests" that are not representative of real-world usage [E7] is a validity risk. If the benchmark tasks do not challenge the model's capabilities, the results will not be meaningful and will not reflect the model's true performance. To address these risks and meet the benchmark design requirements, the following recommended next actions are proposed. The first action is to optimize llama.cpp for 262144 token context. This is a high-priority task, as the slow prompt evaluation is a critical bottleneck. The owner of this task is Alex, and the validation method is to measure prompt evaluation time at 262144 tokens and ensure it is within acceptable limits. The second action is to evaluate GPU upgrade or model quantization. This is also a high-priority task, as the VRAM constraint is a fundamental limitation. The owner is Priya, and the validation method is to compare VRAM usage of different model quantizations and assess if the R9700 can handle 262144 context. The third action is to implement dashboard screenshots and visualizations. This is a medium-priority task, as the dashboard is essential for monitoring benchmark performance. The owner is the Dashboard Team, and the validation method is to verify that the dashboard includes screenshots of throughput, reliability, latency, MTP acceptance, output quality, model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison. The fourth action is to redesign benchmark tasks for realism. This is a high-priority task, as the current tasks are not representative of real-world usage. The owner is the Benchmark Team, and the validation method is to conduct user testing with new tasks and ensure they produce long final outputs and include reasoning. The fifth action is to implement progress reporting for long outputs. This is a medium-priority task, as the lack of progress reporting is a reliability risk. The owner is DevOps, and the validation method is to run long outputs and verify that progress, artifacts, and partial failure reporting are working correctly. The sixth action is to audit WorkDash data processing and storage. This is a high-priority task, as the privacy risk is significant. The owner is Security, and the validation method is to verify that raw messages are stored as local artifacts only and redacted previews are stored in SQLite. The seventh action is to implement --keep-going flag for benchmark runner. This is a low-priority task, as it is a minor feature. The owner is DevOps, and the validation method is to run benchmark tasks with and without --keep-going and verify that tasks stop after failure unless flag is supplied. The eighth action is to implement traffic type comparison without app-specific integrations. This is a medium-priority task, as it is a core requirement. The owner is Support, and the validation method is to compare traffic types from OpenWebUI, AgentSSH, Cline, opencode, and WorkDash and verify that no app-specific integrations are required. The ninth action is to draft public writeup focusing on aggregate model behavior. This is a medium-priority task, as it is a deliverable. The owner is Planning, and the validation method is to review public writeup to ensure it describes aggregate model behavior and does not include private source data. The tenth action is to implement comprehensive metric capture for every benchmark run. This is a high-priority task, as it is essential for benchmarking. The owner is Maintenance, and the validation method is to run benchmark tasks and verify that all required metrics are captured and stored correctly. In addition to the recommended next actions, there are several other considerations that must be addressed to ensure the success of the benchmarking effort. One such consideration is the reasoning budget. The active model, Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf, is configured with a --reasoning-budget of 8192 [E6]. This parameter limits the depth of the model's reasoning process, which may impact the quality of long outputs. It is important to monitor the reasoning budget during benchmark runs to ensure that the model is not being constrained by this parameter. Another consideration is the MTP acceptance rate. The server logs indicate that the MTP profile is performing well, with a draft_n_accepted / draft_n ratio of 0.75 to 0.90 [E4]. However, this rate may vary depending on the context window size and the complexity of the task. It is important to monitor the MTP acceptance rate during benchmark runs to ensure that the MTP mechanism is functioning effectively. The dashboard visualization requirements are also critical. The dashboard must include screenshots showing throughput, reliability, latency, MTP acceptance, and output quality [E3]. Additionally, it must feature a model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison [E10]. These visualizations will allow users to quickly assess model performance and identify areas for improvement. The context-fit table is particularly important, as it will allow users to understand how different context sizes impact performance. The MTP vs non-MTP comparison is also important, as it will allow users to understand the performance benefits of the MTP mechanism. The task design requirements are equally critical. The current benchmark tasks are considered "toy tests" and are not representative of real-world usage [E7]. Real tasks should produce long final outputs and include reasoning [E7]. This requires a redesign of the benchmark suite to include more complex, realistic tasks that challenge the model's capabilities. The tasks should be designed to test the model's ability to handle long contexts, complex reasoning, and multi-step tasks. The tasks should also be designed to test the model's ability to handle different traffic types, such as OpenWebUI, AgentSSH, Cline, opencode, and WorkDash [E11]. The reliability and failure handling requirements are also important. Each benchmark task should stop after failure unless --keep-going is supplied [E12]. This ensures that failures are not masked and that the system can be easily restarted for debugging. Additionally, because long outputs can run for many minutes, progress, artifacts, and partial failure reporting must be implemented [E14]. This ensures that users and systems are not left in a state of uncertainty during long-running tasks. The progress reporting should include information such as the current token generation rate, the estimated time to completion, and the current output length. The partial failure reporting should include information such as the error message, the stack trace, and the current state of the task. The privacy and data storage requirements are also critical. Raw messages must be stored as local artifacts only [E9], with redacted previews stored in SQLite [E9]. This ensures that sensitive information is never exposed in the database or in the public writeup. The redaction process should be automated and should remove all sensitive information, such as email addresses, passwords, and personal identifiers. The redacted previews should be stored in SQLite, which is a lightweight database that can be easily queried and analyzed. The local artifacts should be stored in a secure location, such as an encrypted disk, to prevent unauthorized access. The public writeup requirements are also important. The first publishable public writeup can describe aggregate model behavior [E13]. However, private source data must not leave the lab [E13]. This means that the public writeup must focus on high-level metrics and trends, without including any specific details about the models, tasks, or hardware used. The public writeup should include information such as the average throughput, the average latency, the average MTP acceptance rate, and the average output quality. The public writeup should also include information such as the performance of the model under different context sizes and the performance of the MTP mechanism compared to the non-MTP mechanism. The traffic type comparison requirements are also critical. The benchmarking system must compare traffic types from OpenWebUI, AgentSSH, Cline, opencode, and WorkDash [E11]. However, it must do so without requiring app-specific integrations [E11]. This architectural constraint means that the benchmarking system must be able to capture and analyze traffic at a network or protocol level, rather than relying on hooks or APIs within each application. This approach ensures that the benchmarking system remains agnostic and can be used to evaluate any traffic type without modification. The traffic type comparison should include information such as the average throughput, the average latency, the average error rate, and the average output quality for each traffic type. The metric collection requirements are also important. Every benchmark run must capture a comprehensive set of metrics, including model path, quant, context, backend, MTP settings, reasoning budget, prompt tokens, completion tokens, duration, and GPU memory [E8]. These metrics provide the raw data necessary to evaluate model performance and identify bottlenecks. The metric collection should be automated and should capture all metrics for every benchmark run. The metrics should be stored in a centralized database, such as SQLite, for easy querying and analysis. The metrics should also be visualized on the dashboard, as described above. The VRAM constraint on the R9700 is a fundamental limitation that must be addressed. The R9700 provides 32624 MiB of VRAM [E2], but the model load for Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf consumes approximately 31016 MiB after warmup [E2]. This leaves only about 1608 MiB of VRAM for the KV cache, prompt processing, and other overhead. When Alex reports that llama.cpp prompt evaluation is slow after enabling a 262144 token context [E1], it is a direct consequence of this VRAM constraint. A 262144 token context requires a massive KV cache, which cannot fit in the remaining 1608 MiB. Consequently, the system is likely forced to offload KV cache to system RAM or swap, resulting in severe performance degradation. This finding dictates that any benchmark involving long contexts must either use a smaller context window, a model with a smaller footprint, or a GPU with significantly more VRAM. One possible solution is to use a smaller context window, such as 32768 tokens, which would require less VRAM for the KV cache. Another possible solution is to use a model with a smaller footprint, such as a quantized model. A third possible solution is to use a GPU with significantly more VRAM, such as an A100 or H100. The MTP mechanism is another area of technical interest. The server logs indicate that for the current MTP profile, the ratio of draft_n_accepted / draft_n is usually around 0.75 to 0.90 [E4]. This high acceptance rate means that 75% to 90% of the draft tokens generated by the MTP mechanism are accepted by the main model, significantly accelerating throughput. However, the slow prompt evaluation at 262144 tokens [E1] suggests that the MTP acceleration is being bottlenecked by the context window size, not by the MTP mechanism itself. The reasoning budget of 8192 [E6] is also a critical parameter, as it limits the depth of the model's reasoning process, which may impact the quality of long outputs. The dashboard requirement for a context-fit table [E10] suggests that the team intends to analyze how different context sizes impact performance, which will be crucial for understanding the system's limits. The traffic types comparison is another technical requirement. The benchmarking system must compare traffic types from OpenWebUI, AgentSSH, Cline, opencode, and WorkDash [E11]. However, it must do so without requiring app-specific integrations [E11]. This architectural constraint means that the benchmarking system must be able to capture and analyze traffic at a network or protocol level, rather than relying on hooks or APIs within each application. This approach ensures that the benchmarking system remains agnostic and can be used to evaluate any traffic type without modification. The traffic types comparison should include information such as the average throughput, the average latency, the average error rate, and the average output quality for each traffic type. The privacy risks are significant. WorkDash processes email and Teams content as part of its normal operations [E5]. This introduces a significant privacy risk, as the system has access to potentially sensitive internal communications. The project note explicitly states that benchmark outputs must stay private [E5], indicating that the data generated by the benchmarking process is considered confidential and must not be exposed. To mitigate privacy risks, the system must adhere to a strict data storage architecture. Raw messages must be stored as local artifacts only [E9], meaning they should not be transmitted over the network or stored in shared databases. Instead, redacted previews of these messages should be stored in SQLite [E9]. This approach ensures that sensitive information is never exposed in the database, while still allowing for basic analysis and auditing. The planning note emphasizes that private source data must not leave the lab [E13]. This is a critical constraint for the public writeup, which can describe aggregate model behavior but cannot include any private source data. This means that the benchmarking system must have robust data sanitization and redaction mechanisms in place to prevent accidental data leakage. The benchmark design requirements are derived from the user complaints, dashboard notes, and maintenance tasks captured during the week. Every benchmark run must capture a comprehensive set of metrics, including model path, quant, context, backend, MTP settings, reasoning budget, prompt tokens, completion tokens, duration, and GPU memory [E8]. These metrics provide the raw data necessary to evaluate model performance and identify bottlenecks. The benchmark dashboard must include screenshots showing throughput, reliability, latency, MTP acceptance, and output quality [E3]. Additionally, it must feature a model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison [E10]. These visualizations will allow users to quickly assess model performance and identify areas for improvement. The current benchmark tasks are considered "toy tests" and are not representative of real-world usage [E7]. Real tasks should produce long final outputs and include reasoning [E7]. This requires a redesign of the benchmark suite to include more complex, realistic tasks that challenge the model's capabilities. Each benchmark task should stop after failure unless --keep-going is supplied [E12]. This ensures that failures are not masked and that the system can be easily restarted for debugging. Additionally, because long outputs can run for many minutes, progress, artifacts, and partial failure reporting must be implemented [E14]. This ensures that users and systems are not left in a state of uncertainty during long-running tasks. The reporting requirements define how benchmark results should be documented and shared, both internally and externally. Internal reporting must include the comprehensive set of metrics captured during each benchmark run [E8]. The dashboard must provide detailed visualizations of these metrics, including the model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison [E10]. The first publishable public writeup can describe aggregate model behavior [E13]. However, private source data must not leave the lab [E13]. This means that the public writeup must focus on high-level metrics and trends, without including any specific details about the models, tasks, or hardware used. Raw messages must be stored as local artifacts only [E9], with redacted previews stored in SQLite [E9]. This ensures that sensitive information is never exposed in the database or in the public writeup. The risks identified in this analysis are multifaceted. The primary risk is the potential leakage of private source data. WorkDash processes email and Teams content [E5], and if the system is not properly isolated, sensitive information could be exposed. The requirement for raw messages to be stored as local artifacts only [E9] and redacted previews in SQLite [E9] mitigates this risk, but strict adherence to these protocols is essential. The slow prompt evaluation at 262144 tokens [E1] is a significant performance risk. The VRAM constraint on the R9700 [E2] makes it impossible to run long contexts without severe degradation. This risk could lead to inaccurate benchmark results and user dissatisfaction. The lack of progress reporting for long outputs [E14] is a reliability risk. If a task runs for many minutes without any feedback, users may assume it has failed and terminate it prematurely. This could lead to incomplete benchmark results and wasted compute resources. The use of "toy tests" that are not representative of real-world usage [E7] is a validity risk. If the benchmark tasks do not challenge the model's capabilities, the results will not be meaningful and will not reflect the model's true performance. To address these risks and meet the benchmark design requirements, the following recommended next actions are proposed. The first action is to optimize llama.cpp for 262144 token context. This is a high-priority task, as the slow prompt evaluation is a critical bottleneck. The owner of this task is Alex, and the validation method is to measure prompt evaluation time at 262144 tokens and ensure it is within acceptable limits. The second action is to evaluate GPU upgrade or model quantization. This is also a high-priority task, as the VRAM constraint is a fundamental limitation. The owner is Priya, and the validation method is to compare VRAM usage of different model quantizations and assess if the R9700 can handle 262144 context. The third action is to implement dashboard screenshots and visualizations. This is a medium-priority task, as the dashboard is essential for monitoring benchmark performance. The owner is the Dashboard Team, and the validation method is to verify that the dashboard includes screenshots of throughput, reliability, latency, MTP acceptance, output quality, model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison. The fourth action is to redesign benchmark tasks for realism. This is a high-priority task, as the current tasks are not representative of real-world usage. The owner is the Benchmark Team, and the validation method is to conduct user testing with new tasks and ensure they produce long final outputs and include reasoning. The fifth action is to implement progress reporting for long outputs. This is a medium-priority task, as the lack of progress reporting is a reliability risk. The owner is DevOps, and the validation method is to run long outputs and verify that progress, artifacts, and partial failure reporting are working correctly. The sixth action is to audit WorkDash data processing and storage. This is a high-priority task, as the privacy risk is significant. The owner is Security, and the validation method is to verify that raw messages are stored as local artifacts only and redacted previews are stored in SQLite. The seventh action is to implement --keep-going flag for benchmark runner. This is a low-priority task, as it is a minor feature. The owner is DevOps, and the validation method is to run benchmark tasks with and without --keep-going and verify that tasks stop after failure unless flag is supplied. The eighth action is to implement traffic type comparison without app-specific integrations. This is a medium-priority task, as it is a core requirement. The owner is Support, and the validation method is to compare traffic types from OpenWebUI, AgentSSH, Cline, opencode, and WorkDash and verify that no app-specific integrations are required. The ninth action is to draft public writeup focusing on aggregate model behavior. This is a medium-priority task, as it is a deliverable. The owner is Planning, and the validation method is to review public writeup to ensure it describes aggregate model behavior and does not include private source data. The tenth action is to implement comprehensive metric capture for every benchmark run. This is a high-priority task, as it is essential for benchmarking. The owner is Maintenance, and the validation method is to run benchmark tasks and verify that all required metrics are captured and stored correctly. In addition to the recommended next actions, there are several other considerations that must be addressed to ensure the success of the benchmarking effort. One such consideration is the reasoning budget. The active model, Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf, is configured with a --reasoning-budget of 8192 [E6]. This parameter limits the depth of the model's reasoning process, which may impact the quality of long outputs. It is important to monitor the reasoning budget during benchmark runs to ensure that the model is not being constrained by this parameter. Another consideration is the MTP acceptance rate. The server logs indicate that the MTP profile is performing well, with a draft_n_accepted / draft_n ratio of 0.75 to 0.90 [E4]. However, this rate may vary depending on the context window size and the complexity of the task. It is important to monitor the MTP acceptance rate during benchmark runs to ensure that the MTP mechanism is functioning effectively. The dashboard visualization requirements are also critical. The dashboard must include screenshots showing throughput, reliability, latency, MTP acceptance, and output quality [E3]. Additionally, it must feature a model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison [E10]. These visualizations will allow users to quickly assess model performance and identify areas for improvement. The context-fit table is particularly important, as it will allow users to understand how different context sizes impact performance. The MTP vs non-MTP comparison is also important, as it will allow users to understand the performance benefits of the MTP mechanism. The task design requirements are equally critical. The current benchmark tasks are considered "toy tests" and are not representative of real-world usage [E7]. Real tasks should produce long final outputs and include reasoning [E7]. This requires a redesign of the benchmark suite to include more complex, realistic tasks that challenge the model's capabilities. The tasks should be designed to test the model's ability to handle long contexts, complex reasoning, and multi-step tasks. The tasks should also be designed to test the model's ability to handle different traffic types, such as OpenWebUI, AgentSSH, Cline, opencode, and WorkDash [E11]. The reliability and failure handling requirements are also important. Each benchmark task should stop after failure unless --keep-going is supplied [E12]. This ensures that failures are not masked and that the system can be easily restarted for debugging. Additionally, because long outputs can run for many minutes, progress, artifacts, and partial failure reporting must be implemented [E14]. This ensures that users and systems are not left in a state of uncertainty during long-running tasks. The progress reporting should include information such as the current token generation rate, the estimated time to completion, and the current output length. The partial failure reporting should include information such as the error message, the stack trace, and the current state of the task. The privacy and data storage requirements are also critical. Raw messages must be stored as local artifacts only [E9], with redacted previews stored in SQLite [E9]. This ensures that sensitive information is never exposed in the database or in the public writeup. The redaction process should be automated and should remove all sensitive information, such as email addresses, passwords, and personal identifiers. The redacted previews should be stored in SQLite, which is a lightweight database that can be easily queried and analyzed. The local artifacts should be stored in a secure location, such as an encrypted disk, to prevent unauthorized access. The public writeup requirements are also important. The first publishable public writeup can describe aggregate model behavior [E13]. However, private source data must not leave the lab [E13]. This means that the public writeup must focus on high-level metrics and trends, without including any specific details about the models, tasks, or hardware used. The public writeup should include information such as the average throughput, the average latency, the average MTP acceptance rate, and the average output quality. The public writeup should also include information such as the performance of the model under different context sizes and the performance of the MTP mechanism compared to the non-MTP mechanism. The traffic type comparison requirements are also critical. The benchmarking system must compare traffic types from OpenWebUI, AgentSSH, Cline, opencode, and WorkDash [E11]. However, it must do so without requiring app-specific integrations [E11]. This architectural constraint means that the benchmarking system must be able to capture and analyze traffic at a network or protocol level, rather than relying on hooks or APIs within each application. This approach ensures that the benchmarking system remains agnostic and can be used to evaluate any traffic type without modification. The traffic type comparison should include information such as the average throughput, the average latency, the average error rate, and the average output quality for each traffic type. The metric collection requirements are also important. Every benchmark run must capture a comprehensive set of metrics, including model path, quant, context, backend, MTP settings, reasoning budget, prompt tokens, completion tokens, duration, and GPU memory [E8]. These metrics provide the raw data necessary to evaluate model performance and identify bottlenecks. The metric collection should be automated and should capture all metrics for every benchmark run. The metrics should be stored in a centralized database, such as SQLite, for easy querying and analysis. The metrics should also be visualized on the dashboard, as described above. The VRAM constraint on the R9700 is a fundamental limitation that must be addressed. The R9700 provides 32624 MiB of VRAM [E2], but the model load for Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf consumes approximately 31016 MiB after warmup [E2]. This leaves only about 1608 MiB of VRAM for the KV cache, prompt processing, and other overhead. When Alex reports that llama.cpp prompt evaluation is slow after enabling a 262144 token context [E1], it is a direct consequence of this VRAM constraint. A 262144 token context requires a massive KV cache, which cannot fit in the remaining 1608 MiB. Consequently, the system is likely forced to offload KV cache to system RAM or swap, resulting in severe performance degradation. This finding dictates that any benchmark involving long contexts must either use a smaller context window, a model with a smaller footprint, or a GPU with significantly more VRAM. One possible solution is to use a smaller context window, such as 32768 tokens, which would require less VRAM for the KV cache. Another possible solution is to use a model with a smaller footprint, such as a quantized model. A third possible solution is to use a GPU with significantly more VRAM, such as an A100 or H100. The MTP mechanism is another area of technical interest. The server logs indicate that for the current MTP profile, the ratio of draft_n_accepted / draft_n is usually around 0.75 to 0.90 [E4]. This high acceptance rate means that 75% to 90% of the draft tokens generated by the MTP mechanism are accepted by the main model, significantly accelerating throughput. However, the slow prompt evaluation at 262144 tokens [E1] suggests that the MTP acceleration is being bottlenecked by the context window size, not by the MTP mechanism itself. The reasoning budget of 8192 [E6] is also a critical parameter, as it limits the depth of the model's reasoning process, which may impact the quality of long outputs. The dashboard requirement for a context-fit table [E10] suggests that the team intends to analyze how different context sizes impact performance, which will be crucial for understanding the system's limits. The traffic types comparison is another technical requirement. The benchmarking system must compare traffic types from OpenWebUI, AgentSSH, Cline, opencode, and WorkDash [E11]. However, it must do so without requiring app-specific integrations [E11]. This architectural constraint means that the benchmarking system must be able to capture and analyze traffic at a network or protocol level, rather than relying on hooks or APIs within each application. This approach ensures that the benchmarking system remains agnostic and can be used to evaluate any traffic type without modification. The traffic types comparison should include information such as the average throughput, the average latency, the average error rate, and the average output quality for each traffic type. The privacy risks are significant. WorkDash processes email and Teams content as part of its normal operations [E5]. This introduces a significant privacy risk, as the system has access to potentially sensitive internal communications. The project note explicitly states that benchmark outputs must stay private [E5], indicating that the data generated by the benchmarking process is considered confidential and must not be exposed. To mitigate privacy risks, the system must adhere to a strict data storage architecture. Raw messages must be stored as local artifacts only [E9], meaning they should not be transmitted over the network or stored in shared databases. Instead, redacted previews of these messages should be stored in SQLite [E9]. This approach ensures that sensitive information is never exposed in the database, while still allowing for basic analysis and auditing. The planning note emphasizes that private source data must not leave the lab [E13]. This is a critical constraint for the public writeup, which can describe aggregate model behavior but cannot include any private source data. This means that the benchmarking system must have robust data sanitization and redaction mechanisms in place to prevent accidental data leakage. The benchmark design requirements are derived from the user complaints, dashboard notes, and maintenance tasks captured during the week. Every benchmark run must capture a comprehensive set of metrics, including model path, quant, context, backend, MTP settings, reasoning budget, prompt tokens, completion tokens, duration, and GPU memory [E8]. These metrics provide the raw data necessary to evaluate model performance and identify bottlenecks. The benchmark dashboard must include screenshots showing throughput, reliability, latency, MTP acceptance, and output quality [E3]. Additionally, it must feature a model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison [E10]. These visualizations will allow users to quickly assess model performance and identify areas for improvement. The current benchmark tasks are considered "toy tests" and are not representative of real-world usage [E7]. Real tasks should produce long final outputs and include reasoning [E7]. This requires a redesign of the benchmark suite to include more complex, realistic tasks that challenge the model's capabilities. Each benchmark task should stop after failure unless --keep-going is supplied [E12]. This ensures that failures are not masked and that the system can be easily restarted for debugging. Additionally, because long outputs can run for many minutes, progress, artifacts, and partial failure reporting must be implemented [E14]. This ensures that users and systems are not left in a state of uncertainty during long-running tasks. The reporting requirements define how benchmark results should be documented and shared, both internally and externally. Internal reporting must include the comprehensive set of metrics captured during each benchmark run [E8]. The dashboard must provide detailed visualizations of these metrics, including the model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison [E10]. The first publishable public writeup can describe aggregate model behavior [E13]. However, private source data must not leave the lab [E13]. This means that the public writeup must focus on high-level metrics and trends, without including any specific details about the models, tasks, or hardware used. Raw messages must be stored as local artifacts only [E9], with redacted previews stored in SQLite [E9]. This ensures that sensitive information is never exposed in the database or in the public writeup. The risks identified in this analysis are multifaceted. The primary risk is the potential leakage of private source data. WorkDash processes email and Teams content [E5], and if the system is not properly isolated, sensitive information could be exposed. The requirement for raw messages to be stored as local artifacts only [E9] and redacted previews in SQLite [E9] mitigates this risk, but strict adherence to these protocols is essential. The slow prompt evaluation at 262144 tokens [E1] is a significant performance risk. The VRAM constraint on the R9700 [E2] makes it impossible to run long contexts without severe degradation. This risk could lead to inaccurate benchmark results and user dissatisfaction. The lack of progress reporting for long outputs [E14] is a reliability risk. If a task runs for many minutes without any feedback, users may assume it has failed and terminate it prematurely. This could lead to incomplete benchmark results and wasted compute resources. The use of "toy tests" that are not representative of real-world usage [E