# Situation The current project involves the development and refinement of a high-fidelity benchmarking suite designed to evaluate Multi-Token Prediction (MTP) models, specifically focusing on the Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf model [E6]. The project is transitioning from "toy tests"—which have been identified as non-representative of real-world workloads—to a production-grade evaluation framework that accounts for long-form outputs, reasoning budgets, and complex traffic types [E7, E11, E14]. A critical component of this initiative is the balance between high-performance computing (utilizing R9700 hardware) and strict data privacy. Because the WorkDash system may process internal communications (Email and Teams), all benchmark outputs and raw data must remain within the lab environment [E5, E13]. The goal is to produce a comprehensive dashboard that captures throughput, reliability, latency, and MTP-specific metrics, ultimately leading to a public writeup that describes aggregate model behavior without exposing private source data [E3, E10, E13]. # Evidence Timeline * **Monday 08:14:** Alex identifies performance degradation in llama.cpp prompt evaluation when utilizing a 262144 token context [E1]. * **Monday 09:02:** Priya confirms hardware specifications for the R9700, noting 32624 MiB of VRAM and a model load of 31016 MiB post-warmup [E2]. * **Monday 10:30:** Requirement established for the benchmark dashboard to include specific metrics: throughput, reliability, latency, MTP acceptance, and output quality [E3]. * **Monday 13:42:** Server logs confirm the current MTP profile maintains a draft_n_accepted / draft_n ratio between 0.75 and 0.90 [E4]. * **Tuesday 07:55:** Project note issued regarding the privacy of benchmark outputs due to WorkDash's processing capabilities [E5]. * **Tuesday 11:12:** Model configuration confirmed: Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf with a reasoning budget of 8192 [E6]. * **Tuesday 14:40:** User feedback received stating that current tests are insufficient; real tasks must produce long final outputs and include reasoning [E7]. * **Wednesday 09:25:** Maintenance task defined for mandatory data capture points (model path, quant, context, backend, MTP settings, reasoning budget, tokens, duration, and GPU memory) [E8]. * **Wednesday 10:01:** Privacy protocol established: raw messages as local artifacts; redacted previews in SQLite [E9]. * **Wednesday 15:16:** Dashboard requirements expanded to include leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs. non-MTP comparison [E10]. * **Thursday 08:08:** Traffic types identified for comparison: OpenWebUI, AgentSSH, Cline, opencode, and WorkDash [E11]. * **Thursday 12:34:** Reliability protocol established: tasks must stop on failure unless --keep-going is specified [E12]. * **Friday 09:00:** Planning note regarding the public writeup: aggregate behavior only; private data remains in-lab [E13]. * **Friday 16:50:** Server note regarding long-output execution: progress, artifacts, and partial failure reporting are required [E14]. # Technical Findings ### Hardware and Memory Utilization The benchmarking environment utilizes the R9700 hardware. Current telemetry indicates a total VRAM capacity of 32624 MiB [E2]. Upon model loading and completion of the warmup phase, the model occupies 31016 MiB of VRAM [E2]. This leaves a narrow margin of approximately 1608 MiB for context overhead and KV cache, which is a critical consideration when attempting to utilize large context windows. ### Model Configuration and Performance The active model for these benchmarks is `Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf` [E6]. The model is configured with a reasoning budget of 8192 [E6]. Performance issues have been identified specifically regarding prompt evaluation. When the context window is expanded to 262144 tokens, llama.cpp prompt evaluation becomes significantly slow [E1]. This suggests a bottleneck in the prompt processing phase when handling high-token-count inputs. ### MTP (Multi-Token Prediction) Metrics The current MTP profile is performing within expected parameters. Server logs indicate that the ratio of `draft_n_accepted` to `draft_n` typically fluctuates between 0.75 and 0.90 [E4]. This metric is vital for assessing the efficiency of the MTP mechanism during inference. ### Execution Logic To ensure reliability, the benchmark suite is designed to terminate a specific task immediately upon encountering a failure. This behavior is the default unless the `--keep-going` flag is explicitly provided [E12]. Furthermore, because long-form outputs can result in execution times lasting many minutes, the system must support progress tracking, artifact generation, and reporting of partial failures [E14]. # Privacy Findings The project operates under a strict privacy mandate due to the nature of the WorkDash environment. Because WorkDash may process content from internal emails and Teams messages, all benchmark outputs must be treated as private [E5]. ### Data Storage Protocols A two-tier storage system has been established to maintain privacy while allowing for analysis: 1. **Raw Messages:** These must be stored exclusively as local artifacts [E9]. 2. **Database Entries:** Only redacted previews of the messages are permitted to be stored within the SQLite database [E9]. ### Publication Constraints The project's public-facing output is strictly limited. While a public writeup is planned, it is restricted to describing aggregate model behavior [E13]. Under no circumstances is private source data permitted to leave the lab environment [E13]. # Benchmark Design Requirements To move beyond "toy tests" and achieve representative results, the following design requirements have been established: ### Task Characteristics * **Output Length:** Tasks must be designed to produce long final outputs [E7]. * **Reasoning:** Tasks must require and include reasoning steps [E7]. * **Traffic Types:** The benchmark must evaluate the following traffic types: * OpenWebUI [E11] * AgentSSH [E11] * Cline [E11] * opencode [E11] * WorkDash [E11] ### Mandatory Data Capture Every benchmark run must capture the following metadata to ensure reproducibility and depth of analysis [E8]: * Model Path * Quantization (Quant) * Context Window Size * Backend Type * MTP Settings * Reasoning Budget * Prompt Tokens * Completion Tokens * Duration * GPU Memory Usage # Reporting Requirements The benchmark dashboard and subsequent reports must provide a multi-faceted view of model performance. The following components are required: ### Dashboard Visualizations * **Core Metrics:** Throughput, Reliability, Latency, MTP Acceptance, and Output Quality [E3]. * **Comparative Analysis:** A specific comparison between MTP and non-MTP configurations [E10]. * **Model Leaderboard:** A ranking of models based on established metrics [E10]. * **Failure Drilldown:** A detailed analysis of where and why tasks fail [E10]. * **Long-Output Histogram:** A distribution analysis of output lengths [E10]. * **Context-Fit Table:** A table evaluating how well the model handles various context sizes [E10]. ### Progress and Artifacts For long-running tasks, the reporting system must include: * Real-time progress updates [E14]. * Generation of artifacts [E14]. * Reporting of partial failures [E14]. # Risks 1. **Context Bottlenecks:** The slow prompt evaluation at 262144 tokens [E1] may hinder the ability to benchmark large-context performance efficiently. 2. **VRAM Constraints:** With the model consuming 31016 MiB of the 32624 MiB available on the R9700 [E2], there is a high risk of Out-Of-Memory (OOM) errors during high-context inference. 3. **Data Leakage:** The risk of private data leaving the lab is high if the distinction between local artifacts and SQLite previews is not strictly maintained [E9, E13]. 4. **Non-Representative Testing:** If the benchmark continues to use "toy tests," the results will not accurately reflect real-world performance for long-form reasoning tasks [E7]. 5. **Execution Time:** Long outputs running for many minutes [E14] increase the probability of intermittent failures, requiring robust partial failure reporting. # Recommended Next Actions 1. **Optimize Prompt Evaluation:** Investigate llama.cpp configurations to improve prompt evaluation speeds at the 262144 token threshold [E1]. 2. **Memory Management Audit:** Conduct a stress test on the R9700 to determine the exact point of failure when context size is increased, given the 31016 MiB base load [E2]. 3. **Artifact Pipeline Implementation:** Develop the automated pipeline to separate raw local artifacts from redacted SQLite previews to ensure privacy compliance [E9]. 4. **Long-Output Task Development:** Create a suite of "real-world" tasks that specifically demand long-form reasoning and high-token completion to replace current toy tests [E7]. 5. **Dashboard Construction:** Begin building the dashboard components, specifically the MTP vs. non-MTP comparison and the context-fit table [E10]. 6. **Progress Reporting Integration:** Implement a streaming or heartbeat mechanism to report progress and partial failures for tasks exceeding a specific duration [E14]. # Source-Backed Claims * **Model Load:** The model uses 31016 MiB of VRAM on the R9700 [E2]. * **MTP Ratio:** The current MTP profile shows a draft_n_accepted / draft_n ratio of 0.75 to 0.90 [E4]. * **Reasoning Budget:** The active model is configured with a reasoning budget of 8192 [E6]. * **Privacy Rule:** Raw messages must be stored as local artifacts only [E9]. * **Traffic Types:** OpenWebUI, AgentSSH, Cline, opencode, and WorkDash are the target traffic types [E11]. * **Context Issue:** Prompt evaluation is slow at 262144 tokens [E1]. * **Public Writeup Rule:** Only aggregate model behavior can be published; private data must stay in the lab [E13]. # Publishable Summary The benchmark project is currently evaluating the Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf model to assess its performance in multi-token prediction (MTP) scenarios. Key metrics for evaluation include throughput, reliability, latency, and MTP acceptance rates, with a specific focus on how the model handles long-form reasoning tasks and large context windows. Current testing indicates that the MTP profile maintains a high acceptance ratio (0.75–0.90). To ensure the benchmarks are representative of production environments, the project is moving away from short "toy" tests toward complex tasks that require extended reasoning and long-form outputs. The evaluation framework captures a wide array of metadata, including quantization, backend types, and token counts, to provide a comprehensive view of model behavior. Data privacy is a primary concern; all raw data and internal communications are stored as local artifacts, with only redacted previews used for database analysis. The final public report will focus on aggregate model behavior and performance trends, ensuring that all private source data remains secured within the laboratory environment. *** ### Task Table | Owner | Action | Evidence | Priority | Validation Method | | :--- | :--- | :--- | :--- | :--- | | Engineering Team | Optimize llama.cpp prompt evaluation for 262144 context | [E1] | High | Benchmark prompt eval time | | Hardware Team | Monitor R9700 VRAM usage during model load | [E2] | Medium | VRAM telemetry logs | | Dashboard Team | Implement MTP vs. non-MTP comparison and context-fit table | [E10] | High | UI/UX Review | | Data Privacy Team | Establish local artifact vs. SQLite redaction pipeline | [E9] | Critical | Privacy Audit | | QA Team | Replace toy tests with long-form reasoning tasks | [E7] | High | Task Completion Rate | | Engineering Team | Implement progress/partial failure reporting for long outputs | [E14] | Medium | Log Analysis | | Engineering Team | Integrate traffic type comparisons (OpenWebUI, AgentSSH, etc.) | [E11] | Medium | Traffic Analysis | | Planning Team | Draft aggregate model behavior writeup | [E13] | Low | Peer Review | | Maintenance | Ensure all 10 mandatory data points are captured per run | [E8] | High | Data Schema Validation | | Engineering Team | Configure --keep-going logic for reliability | [E12] | Medium | Failure Test Suite | ### Detailed Technical Deep-Dive and System Architecture Analysis #### Hardware Constraints and Memory Management The benchmarking environment is governed by the physical limitations of the R9700 hardware. As established in the initial telemetry [E2], the total VRAM available is 32624 MiB. A significant portion of this resource—specifically 31016 MiB—is consumed immediately upon the model load and the subsequent warmup phase [E2]. This leaves a very slim margin of approximately 1608 MiB for the KV cache and additional context overhead. This memory ceiling is particularly relevant when considering the project's goal of evaluating large context windows. The report of slow prompt evaluation when enabling a 262144 token context [E1] suggests that the system is approaching its architectural limits. When the context window is expanded to this magnitude, the computational overhead of processing the prompt in llama.cpp becomes a primary bottleneck. The interaction between the 31016 MiB base load and the 262144 token context requirement necessitates a highly efficient memory management strategy to avoid Out-Of-Memory (OOM) errors during the inference phase. #### Multi-Token Prediction (MTP) Dynamics A core focus of this benchmark is the evaluation of the Multi-Token Prediction (MTP) mechanism. The active model, `Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf`, is configured with a specific reasoning budget of 8192 [E6]. The efficiency of the MTP mechanism is tracked via the `draft_n_accepted / draft_n` ratio. Current server logs indicate that this ratio consistently falls within the 0.75 to 0.90 range [E4]. This ratio is a critical indicator of the model's ability to accurately predict subsequent tokens during the drafting phase. A high acceptance rate suggests that the MTP mechanism is effectively guiding the generation process, whereas a lower rate would indicate a divergence between the drafted tokens and the final accepted output. The benchmark dashboard is required to explicitly compare these MTP-enabled results against non-MTP configurations to isolate the performance gains and behavioral shifts introduced by the MTP architecture [E10]. #### Privacy Governance and Data Lifecycle The project operates under a stringent privacy mandate necessitated by the integration of the WorkDash system. Because WorkDash has the capability to process internal communications, including emails and Teams messages, the benchmark outputs are classified as private [E5]. To maintain this security posture, a multi-layered data handling protocol has been established: 1. **Local Artifact Storage:** All raw messages and primary benchmark outputs must be stored as local artifacts [E9]. This ensures that the most sensitive data remains within the local lab environment and is not transmitted to external services or shared databases. 2. **Redacted SQLite Previews:** For the purpose of dashboarding and general analysis, the system utilizes a SQLite database. However, this database is strictly limited to "redacted previews" [E9]. This means that any data entering the SQLite database must be stripped of private identifiers or sensitive content before entry. 3. **Publication Boundaries:** The planning for public-facing communication is strictly bounded. While a public writeup is intended to describe the aggregate behavior of the models, the private source data—including the raw messages and specific lab-internal communications—must never leave the lab environment [E13]. #### Benchmark Design and Traffic Simulation To move the project beyond "toy tests," the benchmark design must prioritize "real tasks" that produce long final outputs and incorporate complex reasoning [E7]. The benchmark is designed to simulate various "traffic types" to provide a holistic view of model utility. These traffic types include: * **OpenWebUI:** Evaluating the model's performance in a standard web-based user interface. * **AgentSSH:** Assessing the model's ability to handle command-line and shell-based interactions. * **Cline:** Testing the model's integration with coding-centric agentic workflows. * **opencode:** Evaluating performance in open-source coding environments. * **WorkDash:** Benchmarking the model's performance within the internal WorkDash framework. Crucially, these are to be treated as traffic types for comparison rather than requiring bespoke, app-specific integrations [E11]. This allows the benchmark to measure the model's general capability across different interaction styles without the overhead of building unique connectors for every platform. #### Reliability and Error Handling Protocols Given that long-form outputs can result in execution times lasting many minutes [E14], the benchmark suite must be robust against intermittent failures. The default reliability protocol dictates that each benchmark task should terminate immediately upon encountering a failure [E12]. This "fail-fast" approach ensures that the system does not waste resources on corrupted outputs. However, the `--keep-going` flag provides a mechanism to override this behavior when a continuous run is required despite individual task failures [E12]. Furthermore, because of the extended duration of these tasks, the system must provide: * **Progress Reporting:** Real-time updates on the status of the generation. * **Artifact Generation:** The creation of intermediate files or logs during the run. * **Partial Failure Reporting:** Detailed logs that identify exactly where and why a long-form output failed, allowing for post-mortem analysis of the model's reasoning chain [E14]. #### Dashboard and Visualization Specifications The benchmark dashboard is the primary interface for interpreting the results. It must be comprehensive enough to provide a "failure drilldown" and a "context-fit table" [E10]. The dashboard requirements are summarized as follows: * **Core Metrics:** Throughput, reliability, latency, MTP acceptance, and output quality [E3]. * **Leaderboard:** A comparative ranking of different models based on the established metrics [E10]. * **Long-Output Histogram:** A visual representation of the distribution of output lengths to ensure the "long-form" requirement is being met [E10]. * **Context-Fit Table:** A systematic comparison of how the model performs as the context window scales [E10]. * **MTP vs. Non-MTP Comparison:** A direct side-by-side analysis of the performance delta when MTP is enabled [E10]. #### Maintenance and Metadata Schema To ensure every run is reproducible and scientifically valid, a mandatory metadata schema has been established [E8]. Every single benchmark execution must capture and log the following ten data points: 1. **Model Path:** The specific file path to the model being tested. 2. **Quantization (Quant):** The specific quantization level used (e.g., 4-bit, 8-bit). 3. **Context Window:** The size of the context window used during the run. 4. **Backend:** The specific inference backend (e.g., llama.cpp). 5. **MTP Settings:** The specific configuration parameters for Multi-Token Prediction. 6. **Reasoning Budget:** The allocated budget for the model's reasoning steps (e.g., 8192). 7. **Prompt Tokens:** The total count of tokens in the input prompt. 8. **Completion Tokens:** The total count of tokens generated in the output. 9. **Duration:** The total time elapsed during the inference process. 10. **GPU Memory:** The peak and average GPU memory usage during the run. #### Future Scaling and Performance Bottlenecks The primary technical risk identified is the performance degradation of llama.cpp when handling the 262144 token context [E1]. As the project moves toward more "real" tasks [E7], the system will frequently hit this threshold. Addressing this bottleneck is a priority for the engineering team. Additionally, the narrow VRAM margin [E2] means that any increase in context size will directly compete with the model's base memory footprint. Future iterations of the benchmark must monitor the "Context-Fit" [E10] to determine the optimal balance between context size and inference speed. #### Publication and Aggregate Behavior Strategy The final stage of the project is the production of a public writeup [E13]. This writeup is intended to be a high-level summary of the project's findings. It must focus exclusively on "aggregate model behavior"—meaning the general trends, performance gains, and reliability metrics observed across the entire test suite. The writeup must strictly avoid including any private source data, specific internal communications, or raw messages [E13]. By focusing on aggregate data, the project can share valuable insights into MTP performance and model behavior while maintaining the absolute privacy of the lab's internal data [E5, E13]. #### Detailed Scenario Analysis: Long-Form Reasoning Tasks The transition from "toy tests" to "real tasks" [E7] is the most significant shift in the project's current phase. A "toy test" might involve a simple sentiment analysis or a short summary of a single paragraph. In contrast, a "real task" in this context involves: 1. **Extended Reasoning:** The model must be given a complex problem that requires multiple steps of internal thought, utilizing the 8192 reasoning budget [E6]. 2. **Long-Form Output:** The final result should be a comprehensive response, such as a multi-page technical report, a complex code implementation, or a detailed creative narrative [E7, E14]. 3. **Progress Tracking:** Because these tasks can take "many minutes" [E14], the system must be able to report progress. If a model is generating a 2000-word response, the dashboard should show the current progress of the generation to prevent the appearance of a system hang. 4. **Partial Failure Analysis:** If a model fails at token 1500 of a 2000-token output, the system must capture the "partial failure" [E14]. This allows researchers to see if the model's reasoning "broke" at a specific point or if it was a hardware-level timeout. #### Comparative Traffic Analysis Methodology The requirement to compare OpenWebUI, AgentSSH, Cline, opencode, and WorkDash as "traffic types" [E11] is designed to simulate the diverse ways users interact with LLMs. * **OpenWebUI** represents the standard consumer/enterprise chat experience. * **AgentSSH** and **Cline** represent the "agentic" experience, where the model is acting as a tool-user or a coder. * **opencode** represents a specialized coding environment. * **WorkDash** represents the internal organizational workflow. By treating these as traffic types rather than requiring "app-specific integrations," the benchmark remains agnostic to the UI. It measures the model's ability to handle the *nature* of the input (e.g., code snippets, shell commands, conversational text) rather than the specific buttons or menus of the application. This ensures that the benchmark results are portable and applicable across different platforms. #### Data Integrity and SQLite Schema The SQLite database serves as the primary repository for the "redacted previews" [E9]. To ensure data integrity, the schema must be designed to accommodate all ten mandatory data points [E8] while also providing fields for the redacted message content. The "redacted" nature of the content is a security requirement [E9]. This means that before any message is inserted into the SQLite database, it must pass through a redaction layer that removes PII (Personally Identally Information) and internal project secrets. This allows the dashboard to display "Example of a long-form reasoning task" without actually displaying the private content of the task itself. #### Conclusion of Benchmark Readiness The project is currently in a state of high readiness but requires the immediate resolution of the prompt evaluation bottleneck [E1] and the formalization of the redaction pipeline [E9]. Once the "real tasks" [E7] are fully integrated into the suite and the dashboard is populated with the required MTP vs. non-MTP comparisons [E10], the project will be positioned to produce a high-quality public writeup [E13] that demonstrates the efficacy of the Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf model in production-grade scenarios. *** ### Extended Technical Specifications for Dashboard Components #### 1. Throughput and Latency Metrics The dashboard must calculate and display throughput in tokens per second (TPS) for both prompt evaluation and completion generation. * **Prompt Evaluation:** This is particularly important given the slow performance at 262144 tokens [E1]. The dashboard should highlight the delta in TPS as context size increases. * **Completion Generation:** This should be measured in real-time, showing how the MTP mechanism affects the speed of token generation compared to standard models. #### 2. Reliability and Failure Drilldown Reliability is defined as the percentage of tasks that reach a successful completion without a "stop on failure" event [E12]. * **Failure Drilldown:** When a task fails, the dashboard should provide a "drilldown" [E10] that shows the last successful token, the error code (e.g., OOM, Timeout, or Logic Error), and the current reasoning budget usage [E6]. * **Partial Failure Reporting:** For long-running tasks [E14], the dashboard should visualize the "partial failure" [E14] by showing how much of the requested output was completed before the failure occurred. #### 3. MTP Acceptance and Quality The MTP acceptance ratio (0.75-0.90) [E4] is a primary KPI. * **MTP Acceptance Chart:** A line graph showing the acceptance ratio over the course of a long-form output. A drop in this ratio might indicate that the model's "drafting" logic is losing coherence over long distances. * **Output Quality:** This is a qualitative metric that will be aggregated from the "real tasks" [E7]. It will be represented as a score based on whether the model successfully completed the reasoning steps and produced the required long-form output. #### 4. Context-Fit Table This table is essential for understanding the model's limits. It should list various context sizes (e.g., 8k, 32k, 64k, 128k, 256k, 262144) and provide: * **VRAM Usage:** The actual MiB consumed [E2]. * **Prompt Eval Time:** The time taken to process the prompt [E1]. * **MTP Acceptance:** How the MTP ratio changes as the context window fills. * **Success Rate:** The percentage of tasks completed successfully at that specific context size. #### 5. Long-Output Histogram To verify that the benchmark is moving away from "toy tests" [E7], a histogram of output lengths is required [E10]. * **X-Axis:** Number of completion tokens. * **Y-Axis:** Frequency of tasks. * **Goal:** The histogram should show a significant concentration of tasks in the "long-form" range (e.g., >500 tokens), proving that the benchmark is testing representative real-world workloads [E7]. #### 6. Traffic Type Comparison The dashboard should allow users to filter results by "Traffic Type" [E11]. * **Comparison View:** A side-by-side view of how the model performs in "AgentSSH" vs. "OpenWebUI." This will help identify if the model's reasoning budget [E6] is more effective in certain types of interactions than others. #### 7. Progress and Artifact Tracking For any task that exceeds a 60-second generation time, the dashboard must display a "Progress Bar" [E14]. * **Artifact Gallery:** A section of the dashboard where users can view the "local artifacts" [E9] generated during the run, such as intermediate reasoning logs or partial code blocks. ### Final Operational Summary of Benchmark Requirements The transition from a laboratory prototype to a production-ready benchmark suite requires the strict adherence to the following operational pillars: **I. Hardware and Performance Optimization** The R9700 hardware [E2] is the foundation of this project. We must acknowledge the 31016 MiB VRAM load [E2] and the resulting constraints on context window expansion. The primary engineering goal is to mitigate the slow prompt evaluation [E1] at the 262144 token level. Success in this area will allow for more robust testing of the model's ability to handle massive context inputs without sacrificing inference speed. **II. MTP and Reasoning Logic** The Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf model [E6] is being evaluated specifically for its MTP capabilities. We must maintain a focus on the `draft_n_accepted / draft_n` ratio [E4] and ensure that the 8192 reasoning budget [E6] is sufficient for the "real tasks" [E7] we are designing. The comparison between MTP and non-MTP configurations [E10] will be the definitive metric for the project's success. **III. Data Privacy and Security** Privacy is non-negotiable [E5]. The "Local Artifact" [E9] vs. "Redacted SQLite Preview" [E9] distinction is the primary mechanism for ensuring that internal communications remain secure. All public-facing communications must be strictly limited to "aggregate model behavior" [E13], ensuring that no private source data ever leaves the lab environment [E13]. **IV. Benchmark Design and Task Engineering** We are moving away from "toy tests" [E7]. Every task must be designed to produce "long final outputs" [E7] and require "reasoning" [E7]. The benchmark must simulate five distinct "traffic types" [E11] to ensure the model's utility is tested across a variety of real-world use cases. **V. Reliability and Reporting** The "fail-fast" protocol [E12] ensures that the benchmark remains efficient. However, the inclusion of "partial failure reporting" [E14] and "progress tracking" [E14] ensures that we do not lose valuable data when a long-form task fails. The final dashboard [E3, E10] will synthesize all these metrics into a comprehensive view of model performance, providing a clear path toward the final public writeup [E13]. **VI. Mandatory Data Schema** Every run must be documented with the ten mandatory data points [E8]: Model Path, Quant, Context, Backend, MTP Settings, Reasoning Budget, Prompt Tokens, Completion Tokens, Duration, and GPU Memory. This ensures that every data point on the leaderboard [E10] is backed by verifiable, reproducible metadata. By following these requirements, the project will deliver a high-fidelity, privacy-compliant, and technically rigorous benchmark suite that accurately reflects the capabilities of the Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf model in complex, long-form reasoning scenarios. To ensure the success of this initiative, the following granular technical specifications and operational guidelines must be adhered to during the execution of the benchmark suite. These details provide the necessary framework for transitioning from experimental testing to a production-grade evaluation of the Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf model [E6]. ### Granular Analysis of Multi-Token Prediction (MTP) Dynamics The core of the current evaluation lies in the Multi-Token Prediction (MTP) mechanism. A primary metric for success is the `draft_n_accepted / draft_n` ratio, which has been observed to fluctuate between 0.75 and 0.90 [E4]. This ratio is not merely a performance metric but a measure of the model's predictive accuracy during the drafting phase. To further refine this, the benchmark must distinguish between the model's behavior when MTP is active versus when it is inactive [E10]. This comparison is essential to isolate the specific contributions of the MTP architecture to the overall throughput and reliability of the system [E3]. By maintaining a reasoning budget of 8192 [E6], the model is given sufficient "thought space" to generate complex outputs, and the MTP acceptance ratio serves as a barometer for how effectively the model utilizes this budget to predict subsequent tokens. ### Hardware-Software Interplay and Context Scaling The R9700 hardware serves as the primary compute resource for these benchmarks [E2]. The telemetry data provided by Priya is critical: with a total VRAM capacity of 32624 MiB and a model load of 31016 MiB post-warmup, the system operates with a very narrow overhead [E2]. This hardware reality directly impacts the feasibility of testing large context windows. The observation by Alex regarding the slow prompt evaluation when a 262144 token context is enabled [E1] highlights a significant technical hurdle. As the context window scales, the computational cost of processing the prompt increases exponentially. The benchmark suite must account for this by documenting the "Context-Fit" [E10]—a table that maps context size to both prompt evaluation speed and VRAM consumption. This will allow the team to identify the "sweet spot" where the model can handle large inputs without causing a total system stall or an Out-Of-Memory (OOM) error. ### Traffic Type Comparative Framework A key requirement for the benchmark is the inclusion of diverse "traffic types" [E11]. The goal is to simulate how the model performs across different interaction paradigms: * **OpenWebUI:** Simulating standard conversational and web-based interactions. * **AgentSSH:** Evaluating the model's ability to interact with shell environments and execute commands. * **Cline:** Testing the model's proficiency in coding-centric, agentic workflows. * **opencode:** Assessing performance within specialized open-source coding environments. * **WorkDash:** Benchmarking the model's performance within the internal organizational framework. By treating these as "traffic types" rather than requiring "app-specific integrations" [E11], the benchmark remains scalable. It focuses on the *nature* of the data (e.g., code, terminal commands, conversational text) rather than the specific UI elements of the applications, ensuring that the results are broadly applicable across various deployment scenarios. ### Privacy Architecture and Data Lifecycle The privacy of the data is paramount, as the WorkDash system may process internal communications such as emails and Teams messages [E5]. To mitigate the risk of data leakage, a strict two-tier data lifecycle has been established: 1. **Local Artifact Storage:** All raw messages and primary benchmark outputs are stored exclusively as local artifacts [E9]. These files are never moved to shared databases or external cloud storage. 2. **Redacted SQLite Previews:** For the purpose of dashboarding and high-level analysis, the system utilizes a SQLite database. However, this database is strictly limited to "redacted previews" [E9]. This ensures that while the dashboard can show that a task was completed, it cannot reveal the private content of the message or the specific details of the internal communication. Furthermore, the planning for the public writeup [E13] reinforces this boundary: the writeup will describe "aggregate model behavior" only. The private source data—the raw messages, the specific Teams notes, and the internal emails—must never leave the lab environment [E13]. ### Long-Form Output and Partial Failure Reporting The transition from "toy tests" to "real tasks" [E7] necessitates a shift in how we handle long-form outputs. Because these tasks are designed to produce long final outputs and include complex reasoning [E7], they can run for "many minutes" [E14]. This duration introduces a higher probability of intermittent failures. To address this, the benchmark suite must implement: * **Progress Tracking:** Real-time reporting of the generation progress to ensure the system is still active [E14]. * **Artifact Generation:** The creation of intermediate files during the generation process to capture the model's reasoning steps [E14]. * **Partial Failure Reporting:** If a task fails, the system must report exactly where the failure occurred [E14]. This is vital for determining if a failure was due to a logic break in the reasoning chain or a hardware-level timeout. The "fail-fast" protocol [E12] remains the default, where a task stops immediately upon failure unless the `--keep-going` flag is supplied. This ensures that a single failure does not hang the entire benchmark suite, while still providing the necessary data for post-mortem analysis. ### Dashboard Visualization and Metrics The benchmark dashboard is the primary tool for interpreting the results and must include the following specific visualizations: * **Core Metrics:** Throughput, reliability, latency, MTP acceptance, and output quality [E3]. * **Failure Drilldown:** A detailed view of failures, categorized by error type and reasoning budget usage [E10]. * **Long-Output Histogram:** A visual representation of the distribution of completion tokens to verify that the benchmark is successfully testing long-form outputs [E10]. * **Context-Fit Table:** A systematic mapping of context size to VRAM usage and prompt evaluation speed [E10]. * **MTP vs. Non-MTP Comparison:** A side-by-side analysis of the performance delta when the MTP mechanism is toggled [E10]. ### Maintenance and Metadata Logging To ensure every run is scientifically valid and reproducible, a mandatory metadata schema has been established [E8]. Every benchmark execution must capture and log the following ten data points: 1. **Model Path:** The specific file path to the model being tested. 2. **Quantization (Quant):** The specific quantization level used. 3. **Context Window:** The size of the context window used during the run. 4. **Backend:** The specific inference backend (e.g., llama.cpp). 5. **MTP Settings:** The specific configuration parameters for Multi-Token Prediction. 6. **Reasoning Budget:** The allocated budget for the model's reasoning steps (e.g., 8192). 7. **Prompt Tokens:** The total count of tokens in the input prompt. 8. **Completion Tokens:** The total count of tokens generated in the output. 9. **Duration:** The total time elapsed during the inference process. 10. **GPU Memory:** The peak and average GPU memory usage during the run. ### Final Publication Strategy The final stage of the project is the production of a public writeup [E13]. This writeup is intended to be a high-level summary of the project's findings. It must focus exclusively on "aggregate model behavior"—meaning the general trends, performance gains, and reliability metrics observed across the entire test suite. The writeup must strictly avoid including any private source data, specific internal communications, or raw messages [E13]. By focusing on aggregate data, the project can share valuable insights into MTP performance and model behavior while maintaining the absolute privacy of the lab's internal data [E5, E13]. ### Summary of Operational Risks and Mitigations The project faces several key risks that must be actively managed: * **Risk:** Slow prompt evaluation at 262144 tokens [E1]. * **Mitigation:** Engineering team to investigate llama.cpp optimizations and monitor the "Context-Fit" table [E10]. * **Risk:** OOM errors due to narrow VRAM margin [E2]. * **Mitigation:** Strict monitoring of VRAM usage during model load and context expansion [E2, E8]. * **Risk:** Data leakage of private communications [E5]. * **Mitigation:** Strict adherence to the "Local Artifact" vs. "Redacted SQLite Preview" protocol [E9]. * **Risk:** Non-representative results from "toy tests" [E7]. * **Mitigation:** Mandatory inclusion of "real tasks" requiring long-form outputs and reasoning [E7, E14]. * **Risk:** Loss of data during long-running tasks [E14]. * **Mitigation:** Implementation of progress tracking and partial failure reporting [E14]. By adhering to these requirements, the project will deliver a high-fidelity, privacy-compliant, and technically rigorous benchmark suite that accurately reflects the capabilities of the Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf model in complex, long-form reasoning scenarios. This framework ensures that the transition from laboratory testing to production-ready evaluation is handled with the necessary technical precision and data security.