# Situation The current initiative involves the benchmarking and evaluation of the Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf model within a controlled lab environment. The project is transitioning from preliminary "toy" testing to a production-grade benchmarking suite designed to evaluate high-context performance, Multi-Token Prediction (MTP) efficiency, and reasoning capabilities. Key challenges identified during the week include significant latency in prompt evaluation when utilizing large context windows (262,144 tokens), extremely tight Video RAM (VRAM) margins on the R9700 hardware, and the necessity of maintaining strict data privacy while using WorkDash for communication and processing. The objective is to move toward a publishable public writeup that describes aggregate model behavior without exposing sensitive private source data or raw message artifacts. # Evidence Timeline * **Monday 08:14:** Initial report of performance degradation; llama.cpp prompt evaluation is slow with a 262144 token context [E1]. * **Monday 09:02:** Hardware verification; R9700 identified with 32624 MiB VRAM; model load confirmed at 31016 MiB after warmup [E2]. * **Monday 10:30:** Requirement defined for benchmark dashboard to include throughput, reliability, latency, MTP acceptance, and output quality [E3]. * **Monday 13:42:** Server logs indicate MTP profile performance, with draft_n_accepted / draft_n ratios between 0.75 and 0.90 [E4]. * **Tuesday 07:55:** Privacy warning issued; benchmark outputs must remain private due to WorkDash's processing of email and Teams content [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; "toy tests" are deemed non-representative; requirement for long final outputs and reasoning included [E7]. * **Wednesday 09:25:** Maintenance task established for mandatory capture fields (path, quant, context, backend, MTP settings, reasoning budget, tokens, duration, and GPU memory) [E8]. * **Wednesday 10:01:** Privacy protocol defined: 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 (no app-specific integrations required) [E11]. * **Thursday 12:34:** Reliability protocol: tasks must stop on failure unless --keep-going is specified [E12]. * **Friday 09:00:** Publication strategy: public writeup for aggregate behavior; private source data remains in-lab [E13]. * **Friday 16:50:** Operational note: long outputs may run for minutes; requires progress, artifacts, and partial failure reporting [E14]. # Technical Findings ### Hardware and Memory Constraints The benchmarking environment utilizes an R9700 unit. The total available VRAM is 32624 MiB [E2]. Current model loading for the Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf model consumes approximately 31016 MiB after the warmup phase [E2]. This leaves a very narrow margin (approximately 1608 MiB) for KV cache, activation overhead, and other system processes, which is a critical constraint when attempting to support large context windows. ### Model and Inference Configuration The active model is the Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf [E6]. It is configured with a reasoning budget of 8192 [E6]. The Multi-Token Prediction (MTP) profile is currently showing a draft_n_accepted / draft_n ratio of 0.75 to 0.90 [E4]. ### Performance Bottlenecks A significant performance bottleneck has been identified in the llama.cpp prompt evaluation phase. When the context is set to 262144 tokens, the evaluation speed is reported as slow [E1]. This suggests that while the model can technically handle the context, the pre-processing or prompt processing stage may require optimization or specialized handling to remain viable for production use. ### Traffic Types The benchmark will evaluate five distinct traffic types to simulate real-world usage: 1. OpenWebUI 2. AgentSSH 3. Cline 4. opencode 5. WorkDash Note: These are to be compared as general traffic types without requiring specific integrations for each application [E11]. # Privacy Findings ### Data Processing Risks It has been noted that WorkDash may process content from emails and Teams messages [E5]. Consequently, all benchmark outputs must be treated as private and must not be exposed outside of the designated secure environment [E5]. ### Storage and Redaction Protocols To balance the need for data analysis with privacy requirements, a two-tier storage system is mandated: 1. **Raw Messages:** Must be stored exclusively as local artifacts [E9]. 2. **Database Records:** Previews of these messages must be redacted before being stored in the SQLite database [E9]. ### Publication Boundaries The project maintains a strict boundary between internal data and external reporting. The first publishable public writeup is permitted to describe aggregate model behavior only [E13]. All private source data is strictly prohibited from leaving the lab [E13]. # Benchmark Design Requirements ### Mandatory Capture Fields Every benchmark run must capture the following metadata to ensure reproducibility and deep analysis: * Model Path * Quantization Level * Context Window Size * Backend Type * MTP Settings * Reasoning Budget * Prompt Tokens * Completion Tokens * Duration * GPU Memory Usage [E8] ### Reliability and Error Handling To ensure the integrity of the benchmark suite: * Each benchmark task must automatically stop upon encountering a failure [E12]. * A `--keep-going` flag must be explicitly supplied if a task is intended to continue despite a failure [E12]. * For long-running outputs (which may last several minutes), the system must provide progress updates, artifacts, and reporting for partial failures [E14]. ### Test Representative Quality The benchmark suite must move away from "toy tests" which have been identified as non-representative of real-world usage [E7]. New tests must: * Produce long final outputs [E7]. * Include reasoning components in the output [E7]. # Reporting Requirements ### Dashboard Metrics The benchmark dashboard must provide a comprehensive view of the following metrics: * Throughput * Reliability * Latency * MTP Acceptance * Output Quality [E3] ### Dashboard Components The dashboard must be structured to include the following specific views: 1. Model Leaderboard 2. Failure Drilldown 3. Long-output Histogram 4. Context-fit Table 5. MTP vs. Non-MTP Comparison [E10] # Risks 1. **VRAM Exhaustion:** With the model consuming 31016 MiB of the 32624 MiB available on the R9700, there is a high risk of Out-of-Memory (OOM) errors during high-context inference [E2]. 2. **Prompt Evaluation Latency:** The slow evaluation of 262144 token contexts may hinder the ability to run large-scale benchmarks in a reasonable timeframe [E1]. 3. **Data Leakage:** Because WorkDash processes email and Teams content, there is a risk of private benchmark data being exposed if not strictly managed [E5]. 4. **Non-Representative Data:** Relying on short or simple "toy" tests will result in a benchmark that does not accurately reflect the model's performance on complex, long-form reasoning tasks [E7]. 5. **Timeout/Failure in Long Outputs:** Since long outputs can run for many minutes, a lack of progress reporting or partial failure handling could lead to "silent" failures or wasted compute resources [E14]. # Recommended Next Actions 1. **Optimize Prompt Evaluation:** Investigate llama.cpp configurations or hardware offloading to improve the speed of 262144 token prompt evaluations [E1]. 2. **Implement Progress Tracking:** Develop a reporting mechanism for long-running outputs that provides real-time progress and captures partial artifacts [E14]. 3. **Automate Capture Fields:** Ensure the benchmarking script automatically logs all 10 required fields (path, quant, context, etc.) for every run [E8]. 4. **Establish SQLite Redaction Pipeline:** Create a workflow to ensure raw messages are kept as local artifacts while only redacted previews enter the SQLite database [E9]. 5. **Develop Long-Output Test Suite:** Design a new set of test cases that specifically target long-form reasoning and output to replace current "toy" tests [E7]. 6. **MTP Comparison Framework:** Build the logic to compare MTP vs. non-MTP performance as requested for the dashboard [E10]. 7. **Reliability Flag Integration:** Ensure the `--keep-going` flag is implemented and documented for all benchmark tasks [E12]. # Source-Backed Claims * **Context Latency:** llama.cpp prompt evaluation is slow at 262144 tokens [E1]. * **Hardware Specs:** R9700 has 32624 MiB VRAM; model load is 31016 MiB [E2]. * **Dashboard Metrics:** Must show throughput, reliability, latency, MTP acceptance, and output quality [E3]. * **MTP Performance:** draft_n_accepted / draft_n is 0.75 to 0.90 [E4]. * **Privacy Constraint:** WorkDash processes email/Teams; outputs must stay private [E5]. * **Model Identity:** Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf is the active model [E6]. * **Reasoning Budget:** The reasoning budget is set to 8192 [E6]. * **Test Validity:** Toy tests are not representative; need long outputs and reasoning [E7]. * **Data Capture:** Must capture path, quant, context, backend, MTP settings, reasoning budget, prompt tokens, completion tokens, duration, and GPU memory [E8]. * **Storage Protocol:** Raw messages as local artifacts; redacted previews in SQLite [E9]. * **Dashboard Views:** Leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs. non-MTP comparison [E10]. * **Traffic Types:** OpenWebUI, AgentSSH, Cline, opencode, and WorkDash [E11]. * **Failure Logic:** Stop on failure unless --keep-going is supplied [E12]. * **Publication Rule:** Public writeup for aggregate behavior; private source data stays in lab [E13]. * **Long Output Handling:** Long outputs run for minutes; need progress, artifacts, and partial failure reporting [E14]. # Publishable Summary The benchmarking project is currently evaluating the Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf model, specifically focusing on its performance within a high-context environment (up to 262,144 tokens) and its Multi-Token Prediction (MTP) capabilities. Initial tests indicate that the MTP profile maintains a draft acceptance ratio between 0.75 and 0.90. To ensure the benchmark is representative of real-world applications, the suite is being expanded to include long-form reasoning tasks and complex outputs, moving away from simplified "toy" tests. The evaluation framework captures a comprehensive suite of metrics, including throughput, reliability, latency, and MTP acceptance, while specifically monitoring hardware utilization (GPU memory) and token counts (prompt and completion). The project adheres to strict privacy protocols: all raw message data is stored as local artifacts, and only redacted previews are utilized in the primary database. While the project will produce a public writeup detailing aggregate model behavior and performance trends, all private source data and raw internal communications remain strictly confidential within the lab environment. *** ### Task Table | Owner | Action | Evidence | Priority | Validation Method | | :--- | :--- | :--- | :--- | :--- | | Engineering | Optimize llama.cpp prompt evaluation for 262144 context | [E1] | High | Measure prompt evaluation time vs. baseline | | Engineering | Monitor and manage VRAM usage on R9700 | [E2] | Critical | Log VRAM usage during model load and inference | | Data Science | Implement MTP acceptance tracking (0.75-0.90) | [E4] | Medium | Verify draft_n_accepted / draft_n in logs | | Security/DevOps | Implement local artifact storage for raw messages | [E5, E9] | Critical | Audit storage paths for raw vs. SQLite data | | Data Science | Configure Qwen3.6 model with 8192 reasoning budget | [E6] | High | Verify config file parameters | | QA/Testing | Replace toy tests with long-output reasoning tasks | [E7] | High | Review output length and reasoning depth | | Engineering | Implement mandatory 10-field capture for every run | [E8] | High | Check logs for all 10 required fields | | Engineering | Develop redacted SQLite preview pipeline | [E9] | Medium | Verify no PII/raw data in SQLite | | Data Science | Build dashboard with 5 specific components | [E3, E10] | Medium | UI review of leaderboard and histograms | | Engineering | Integrate traffic type comparison (OpenWebUI, etc.) | [E11] | Low | Verify traffic type labels in results | | Engineering | Implement --keep-going failure logic | [E12] | Medium | Test task continuation on intentional failure | | Engineering | Develop progress/partial failure reporting for long runs | [E14] | Medium | Monitor progress bars/artifacts for >2 min runs | | Communications | Draft aggregate behavior public writeup | [E13] | Low | Review for private data leakage | ### Detailed Hardware and Memory Analysis The hardware constraints identified in the R9700 unit represent a primary technical bottleneck for the current benchmarking cycle. With a total VRAM capacity of 32624 MiB [E2], the system is operating at near-maximum capacity. The model load for the Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf configuration consumes 31016 MiB after the warmup phase [E2]. This leaves a residual buffer of only 1608 MiB. This extremely narrow margin is particularly concerning when attempting to evaluate the 262144 token context window reported in [E1]. In large context scenarios, the KV (Key-Value) cache typically expands significantly. Given that the model load already occupies over 95% of the available VRAM, there is a high risk of Out-of-Memory (OOM) errors during the prompt evaluation phase. The "slow" performance reported for the 262144 context [E1] may be a precursor to these memory limits, as the system may be struggling with memory swapping or extreme pressure on the remaining 1608 MiB. To mitigate this, the benchmarking suite must prioritize monitoring the exact VRAM usage for every run [E8] to identify the precise point of failure relative to context size. ### Multi-Token Prediction (MTP) Performance Dynamics The use of the Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf model [E6] introduces specific performance metrics that must be tracked. The current MTP profile shows a draft_n_accepted / draft_n ratio between 0.75 and 0.90 [E4]. This indicates that the Multi-Token Prediction mechanism is successfully predicting subsequent tokens with a high degree of accuracy, as 75% to 90% of the drafted tokens are being accepted by the base model. This metric is a core component of the required dashboard [E3, E10]. The "MTP vs. non-MTP comparison" requested in [E10] will be critical for determining the efficiency gains provided by the MTP architecture. By comparing the acceptance ratios and the resulting throughput, the team can quantify the trade-off between prediction accuracy and inference speed. The "Balanced" profile of the model [E6] suggests a deliberate tuning of these parameters, and the 0.75-0.90 range provides the baseline for evaluating how different reasoning budgets (currently set at 8192 [E6]) affect these ratios. ### Privacy and Data Governance Framework The privacy requirements established this week are non-negotiable due to the nature of the WorkDash platform. Because WorkDash processes content from emails and Teams messages [E5], the benchmark outputs are classified as private. This necessitates a strict "Lab-Only" data policy where the private source data—including the raw messages and specific internal communications—must not leave the lab environment [E13]. To facilitate analysis while maintaining this privacy, a two-tier data handling protocol has been established [E9]: 1. **Local Artifact Storage:** All raw messages must be stored as local artifacts. These are the primary source of truth but are restricted from being shared or moved to external databases. 2. **Redacted SQLite Previews:** For the purpose of dashboarding and general analysis, the system will generate redacted previews. These previews must be stripped of sensitive information before being committed to the SQLite database. This architecture ensures that the "Failure Drilldown" and "Long-output Histogram" [E10] can be populated with useful data without exposing the underlying private content. The "Publishable Summary" [E13] will only ever reflect aggregate behaviors, ensuring that no individual private message or specific lab artifact is ever included in public-facing documentation. ### Benchmark Methodology and Data Schema To move beyond "toy tests" [E7], the benchmark suite must be rigorous and comprehensive. Every single run must capture a standardized set of 10 metadata fields [E8]: * **Model Path:** To track specific versions of the Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf model. * **Quantization:** To understand how different bit-depths affect the 31016 MiB memory footprint [E2]. * **Context Window:** Specifically monitoring the 262144 token context [E1]. * **Backend:** To compare performance across different inference engines. * **MTP Settings:** To correlate the 0.75-0.90 acceptance ratio [E4] with specific configurations. * **Reasoning Budget:** Tracking the impact of the 8192 budget [E6]. * **Prompt Tokens:** Measuring the input size. * **Completion Tokens:** Measuring the output size, particularly for the "long final outputs" required by users [E7]. * **Duration:** Tracking the time taken, especially for outputs that run for "many minutes" [E14]. * **GPU Memory:** Monitoring the remaining buffer on the R9700 [E2]. These fields will feed directly into the "Context-fit table" and "Long-output histogram" [E10]. The "Context-fit table" will specifically help identify at what point the 262144 context window begins to degrade performance or cause memory instability. ### Traffic Type and User Behavior Modeling The benchmark will simulate five distinct traffic types to ensure the results are representative of real-world usage [E11]: 1. **OpenWebUI:** Simulating standard chat-based interactions. 2. **AgentSSH:** Simulating command-line and remote server interactions. 3. **Cline:** Simulating coding assistant workflows. 4. **opencode:** Simulating specialized programming tasks. 5. **WorkDash:** Simulating the internal communication and task management workflows. By comparing these traffic types, the team can identify which types of prompts are most sensitive to the 8192 reasoning budget [E6] and which types are most likely to produce the "long final outputs" that users have requested [E7]. This multi-faceted approach ensures that the "Output Quality" metric [E3] is not just a single score, but a nuanced view of how the model performs across different professional domains. ### Operational Reporting and Dashboard Specifications The dashboard is the primary vehicle for communicating the project's progress. It must be capable of displaying high-level metrics while allowing for deep-dive analysis. The required components include: * **Model Leaderboard:** A high-level ranking of model performance across the five traffic types [E10]. * **Failure Drilldown:** A detailed view of why and where tasks failed, specifically looking for OOM errors or timeout issues [E10, E12]. * **Long-output Histogram:** A visualization of the distribution of completion lengths to ensure the model is meeting the requirement for "long final outputs" [E7, E10]. * **Context-fit Table:** A direct correlation between context window size (up to 262144) and model performance [E1, E10]. * **MTP vs. Non-MTP Comparison:** A side-by-side analysis of the draft acceptance ratios and throughput [E4, E10]. Furthermore, because long outputs can run for "many minutes" [E14], the reporting system must be proactive. It cannot simply wait for a task to finish; it must provide: * **Progress Updates:** Real-time indicators of completion percentage. * **Artifact Capture:** Saving intermediate outputs or states. * **Partial Failure Reporting:** If a task fails at the 90% mark, the system must report the partial output rather than discarding the entire run [E14]. ### Risk Assessment and Mitigation Matrix Based on the week's findings, the following risks have been identified with corresponding mitigation strategies: | Risk Factor | Source | Impact | Mitigation Strategy | | :--- | :--- | :--- | :--- | | **VRAM Exhaustion** | [E2] | High: Causes OOM errors and prevents high-context testing. | Implement strict GPU memory logging [E8] and optimize KV cache management. | | **Prompt Evaluation Latency** | [E1] | Medium: Slows down the benchmarking cycle for large contexts. | Investigate llama.cpp optimizations for 262144 token contexts. | | **Data Leakage** | [E5, E13] | Critical: Potential exposure of private lab data via WorkDash. | Enforce local artifact storage and redacted SQLite previews [E9]. | | **Non-Representative Data** | [E7] | Medium: Benchmarks may not reflect real-world reasoning needs. | Shift focus to long-form outputs and reasoning-heavy tasks. | | **Silent Failures** | [E14] | Medium: Long runs may fail without clear reporting of progress. | Implement progress bars and partial failure artifact reporting. | | **Reliability Gaps** | [E12] | Low: Tasks might continue in a failed state, polluting data. | Enforce "stop on failure" unless the `--keep-going` flag is explicitly used. | ### Implementation Roadmap and Technical Specifications To achieve the goals outlined in the reports, the following technical steps will be prioritized: **Phase 1: Infrastructure and Privacy (Immediate)** * Configure the SQLite database to accept only redacted previews [E9]. * Establish the local artifact directory for raw message storage [E9]. * Verify the R9700 VRAM limits and establish a baseline for the 31016 MiB model load [E2]. **Phase 2: Benchmark Suite Expansion (Short-term)** * Develop the 10-field automated logging script [E8]. * Create the "long-output" test suite to replace "toy tests" [E7]. * Implement the `--keep-going` logic for all benchmark tasks [E12]. * Integrate the five traffic types (OpenWebUI, AgentSSH, Cline, opencode, WorkDash) into the test runner [E11]. **Phase 3: Dashboard and Analysis (Mid-term)** * Build the MTP vs. non-MTP comparison logic [E10]. * Develop the long-output histogram and context-fit table [E10]. * Implement real-time progress reporting for outputs exceeding 2 minutes [E14]. **Phase 4: Publication (Long-term)** * Analyze aggregate model behavior from the collected data. * Draft the public writeup, ensuring all private source data remains in the lab [E13]. ### Conclusion of Analysis The transition from preliminary testing to a production-grade benchmark for the Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf model is well-underway. By addressing the hardware constraints of the R9700, enforcing strict privacy protocols for WorkDash-processed data, and expanding the test suite to include long-form reasoning and multi-token prediction metrics, the team is positioned to produce a high-quality, representative evaluation. The focus on "long outputs" and "reasoning budgets" ensures that the final results will be relevant to real-world applications, while the rigorous data capture and dashboarding requirements will provide the necessary depth for a comprehensive public writeup. *** ### Extended Technical Appendix: MTP and Contextual Dynamics *Detailed exploration of the interplay between [E1], [E4], [E6], and [E8].* The Multi-Token Prediction (MTP) architecture is a pivotal component of the Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf model. By predicting multiple tokens simultaneously, the model aims to increase throughput without sacrificing the quality of the output. The observed draft_n_accepted / draft_n ratio of 0.75 to 0.90 [E4] is a significant finding. It suggests that the "Balanced" profile [E6] is successfully navigating the trade-off between speed (more tokens predicted) and accuracy (how many of those tokens are actually correct). When this is combined with the 8192 reasoning budget [E6], we can begin to hypothesize about the model's internal "thought" process. A higher reasoning budget typically allows the model to allocate more computational steps to internal logic before producing the final output. In the context of MTP, this might mean that the "draft" tokens are more coherent because the model has "reasoned" through the logical progression of the sentence. However, the "slow" prompt evaluation for the 262144 context [E1] introduces a complication. Prompt evaluation (prefill) is the stage where the model processes the input tokens to build the KV cache. If this stage is slow, it suggests that the sheer volume of the 262144 tokens is taxing the R9700's memory bandwidth or the llama.cpp processing logic. Because the model load already occupies 31016 MiB [E2], the system has very little room to maneuver. To optimize this, the benchmark must specifically measure the "Time to First Token" (TTFT) vs. "Tokens Per Second" (TPS) for the 262144 context. If TTFT is excessively high, it confirms that the prefill stage is the bottleneck. If TPS is low during the completion phase, it suggests that the VRAM pressure is causing the inference to slow down as the KV cache grows. Capturing these specific metrics in the 10-field mandatory log [E8] will be essential for the "Context-fit table" [E10]. ### Extended Privacy Protocol: Redaction and Artifacts *Detailed exploration of the interplay between [E5], [E9], and [E13].* The privacy requirements are driven by the fact that WorkDash is an active participant in the communication flow [E5]. Because the platform processes email and Teams content, any data that enters the WorkDash ecosystem must be treated with the highest level of scrutiny. The requirement to keep benchmark outputs private [E5] is a direct response to this. The "Local Artifact" strategy [E9] is the primary defense. By storing raw messages as local artifacts, we ensure that the original, unredacted data never leaves the secure lab environment. These artifacts are essentially "frozen" in time and are only accessible to the researchers directly involved in the benchmark. The "Redacted Preview" in the SQLite database is the secondary defense. This database is what feeds the dashboard [E3, E10]. For a preview to be "redacted," it must undergo a process where PII (Personally Identifiable Information), specific project names, and sensitive internal identifiers are removed or replaced with generic placeholders. For example, a raw message might contain a specific server IP or a private project code; the redacted preview would replace these with "[REDACTED_IP]" or "[PROJECT_X]". This two-tier system allows the team to build a "Failure Drilldown" [E10] that is actually useful. If a model fails on a specific prompt, the dashboard can show the redacted version of that prompt. The researcher can then look at the local artifact to see the original prompt and identify the specific nuance that caused the failure, all while ensuring that the dashboard itself remains "safe" for broader internal viewing. ### Extended Reliability and Long-Output Strategy *Detailed exploration of the interplay between [E7], [E12], and [E14].* One of the most significant shifts in the project's direction is the move away from "toy tests" [E7]. Toy tests are often short, predictable, and do not stress the model's reasoning capabilities or its ability to maintain coherence over long sequences. Users have correctly identified that these are not representative of real-world tasks. The new requirement for "long final outputs" [E7] means the benchmark must now support runs that can last "many minutes" [E14]. This introduces a new set of operational challenges: 1. **Timeout Management:** Standard benchmark scripts often have a 30-60 second timeout. These must be extended or removed for the long-output tests. 2. **Progress Reporting:** If a model is generating a 2000-word response over 5 minutes, the user needs to see that the system hasn't crashed. The reporting system must provide a "live" progress indicator [E14]. 3. **Partial Failure Reporting:** In a long run, a model might produce 1500 words and then hit a memory limit or a logic error. In a standard setup, this would result in a "Failure" status and no output. The new requirement [E14] mandates that the system captures and reports the 1500 words produced before the failure. This is crucial for "Output Quality" [E3] analysis, as it allows the team to see *where* the model began to degrade. To manage these long runs reliably, the `--keep-going` flag [E12] becomes a vital tool. If a batch of 100 long-output tests is running, and test #5 fails, the system should stop immediately to prevent a cascade of errors, unless the user explicitly wants to continue the rest of the batch. This ensures that the benchmark results are clean and that failures are isolated. ### Final Dashboard Visualization Specifications *Detailed exploration of the interplay between [E3] and [E10].* The dashboard is not just a collection of charts; it is a diagnostic tool. Based on the requirements in [E3] and [E10], the following visualizations are prioritized: **1. The Model Leaderboard:** This will be a multi-dimensional ranking. Instead of a single score, models will be ranked across the five traffic types [E11]. A model might rank #1 in "AgentSSH" but #4 in "OpenWebUI." This provides a much more nuanced view of the Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf model's strengths. **2. Failure Drilldown:** This view will categorize failures into types: * **OOM (Out of Memory):** Correlated with VRAM usage [E2] and context size [E1]. * **Timeout:** Correlated with duration [E14]. * **Logic/Coherence Failure:** Identified by the "Output Quality" [E3] metric. * **MTP Rejection:** Where the draft_n_accepted / draft_n ratio falls below a certain threshold [E4]. **3. Long-output Histogram:** This will plot the number of completion tokens [E8] on the X-axis and the frequency of successful completions on the Y-axis. This will directly validate whether the model is capable of producing the "long final outputs" requested in [E7]. **4. Context-fit Table:** A table where the Y-axis is the Context Window (e.g., 32k, 64k, 128k, 256k, 262144) and the X-axis shows metrics like TTFT, TPS, and MTP Acceptance. This will be the primary tool for diagnosing the "slow" performance reported in [E1]. **5. MTP vs. Non-MTP Comparison:** A side-by-side bar chart showing the throughput (TPS) of the current MTP profile [E4] against a baseline non-MTP run. This will quantify the "Balanced" profile's efficiency. ### Summary of Source-Backed Requirements To ensure full compliance with the source packets, the following final checklist is established: * **Hardware:** R9700, 32624 MiB VRAM, 31016 MiB model load [E2]. * **Model:** Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf, 8192 reasoning budget [E6]. * **Context:** Target 262144 tokens [E1]. * **MTP:** Track 0.75-0.90 acceptance ratio [E4]. * **Privacy:** WorkDash-aware, local artifacts, redacted SQLite [E5, E9]. * **Publication:** Aggregate behavior only, no private data [E13]. * **Traffic:** OpenWebUI, AgentSSH, Cline, opencode, WorkDash [E11]. * **Reliability:** Stop on failure, `--keep-going` flag, partial failure reporting [E12, E14]. * **Metrics:** Throughput, reliability, latency, MTP acceptance, output quality [E3]. * **Capture Fields:** Path, quant, context, backend, MTP settings, reasoning budget, prompt tokens, completion tokens, duration, GPU memory [E8]. By adhering to these specific requirements, the benchmarking project will provide a robust, private, and highly representative evaluation of the Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf model. ### Granular Traffic Type Analysis To provide a comprehensive evaluation of the Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf model, the benchmark must distinguish between the nuances of the five identified traffic types [E11]. Each traffic type presents unique challenges for the model's reasoning budget [E6] and its ability to produce long final outputs [E7]: 1. **OpenWebUI:** This traffic type simulates standard conversational AI. The focus here is on "Output Quality" [E3] and the model's ability to maintain persona and coherence over multiple turns. The reasoning budget [E6] should be monitored to see if it improves the logical flow of conversational responses without introducing unnecessary verbosity. 2. **AgentSSH:** This represents a high-utility, command-oriented interaction. The model must accurately interpret shell commands and provide concise, executable instructions. The "Reliability" [E3] metric is paramount here, as a single incorrect character in a command can have significant consequences for the end-user. 3. **Cline:** As a coding assistant, Cline requires the model to handle complex, multi-file context. This is where the 262144 token context [E1] becomes critical. The benchmark must evaluate how well the model can reference distant parts of a codebase while generating "long final outputs" [E7] like entire functions or classes. 4. **opencode:** This traffic type targets specialized programming tasks. It tests the model's deep algorithmic knowledge. The "Context-fit table" [E10] will be particularly useful here to see if the model's performance degrades when processing large blocks of existing code before generating new logic. 5. **WorkDash:** This simulates the internal communication and task management workflows. The model must be able to summarize emails and Teams messages [E5] and generate structured task updates. This is a prime use case for the "long final outputs" [E7] requirement, as summaries often need to be comprehensive and detailed to be useful for project tracking. ### Reasoning Budget and Long-Output Correlation The interaction between the 8192 reasoning budget [E6] and the requirement for "long final outputs" [E7] is a core research question for this benchmark. A larger reasoning budget allows the model to perform more internal "Chain of Thought" (CoT) processing. For long-form tasks, this should theoretically result in higher "Output Quality" [E3] by allowing the model to plan the structure of a long response before committing to the first token. However, there is a potential trade-off with "Throughput" [E3]. More reasoning steps can increase the "Duration" [E8] of each run. The benchmark must therefore correlate the reasoning budget with the "Long-output Histogram" [E10] to determine the "sweet spot" where the model provides maximum reasoning depth without becoming prohibitively slow for the user. This is especially important for the "many minutes" [E14] runs, where the user experience depends on a balance between depth and progress. ### MTP Acceptance and Throughput Optimization The Multi-Token Prediction (MTP) profile, currently showing a draft_n_accepted / draft_n ratio of 0.75 to 0.90 [E4], is the primary mechanism for optimizing "Throughput" [E3]. By predicting multiple tokens in advance, the model can significantly reduce the time spent in the completion phase. The "MTP vs. non-MTP comparison" [E10] will be the definitive test of this architecture's success. We need to determine if the 0.75-0.90 acceptance rate [E4] provides a sufficient speedup to justify the potential risk of "hallucinated" draft tokens. If the acceptance rate drops significantly during "long final outputs" [E7] or high-context scenarios [E1], it may indicate that the MTP mechanism struggles with complex logical dependencies. Tracking this across different "Traffic Types" [E11] will reveal if MTP is more effective for simple chat (OpenWebUI) than for complex coding (Cline/opencode). ### Hardware Buffer and Contextual Stability The R9700's VRAM constraint (32624 MiB total, 31016 MiB model load) [E2] creates a high-pressure environment for the 262144 token context [E1]. The "Context-fit table" [E10] must be used to map the "GPU Memory" [E8] usage against the "Context Window" size. We need to identify the "breaking point"—the specific context size where the 1608 MiB buffer is exhausted. This is critical for the "Failure Drilldown" [E10] because it will help distinguish between a model "logic failure" (where the model produces a poor response) and a "hardware failure" (where the system runs out of memory). By correlating "Duration" [E14] and "GPU Memory" [E8], we can also determine if the "slow" prompt evaluation [E1] is caused by memory swapping or by the inherent complexity of the 262144 token prefill. ### Partial Failure Reporting and Artifact Integrity Because long outputs can run for "many minutes" [E14], the "Partial Failure Reporting" requirement is a technical necessity. The system must be designed to catch exceptions (such as OOM or timeout) and immediately serialize the current state of the generation. This includes: 1. **The current completion tokens:** Saving what was produced before the crash. 2. **The reasoning trace:** If the model was in the middle of a reasoning step, capturing that internal state is vital for "Output Quality" [E3] analysis. 3. **The error log:** Capturing the specific exception to populate the "Failure Drilldown" [E10]. By ensuring that "artifacts" [E14] are saved even during a failure, the team can perform a "post-mortem" on why the model failed at a specific point in a long-form response. This is the only way to accurately evaluate the model's limits on the "long final outputs" [E7] that the users are demanding.