# Situation

The WorkDash internal benchmarking initiative has entered a critical operational phase following a concentrated week of cross-functional communications spanning email, Teams channels, server logs, and project documentation. The primary objective is to establish a rigorous, private, and highly detailed evaluation pipeline for a speculative decoding-enabled large language model (Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf) running on an AMD R9700 GPU. The initiative must balance aggressive context window expansion (262,144 tokens) with acceptable prompt evaluation latency, maintain strict data privacy boundaries given WorkDash's ingestion of email and Teams content, and produce a dashboard that accurately reflects real-world traffic patterns across multiple client applications (OpenWebUI, AgentSSH, Cline, opencode, and WorkDash itself).

Current operational status indicates that the benchmarking environment is actively loaded and processing traffic, but several systemic friction points have emerged. Prompt evaluation latency has increased significantly after enabling the maximum context window, likely due to KV-cache management overhead and attention mechanism scaling. VRAM utilization is operating near capacity (~31,016 MiB of 32,624 MiB available), leaving minimal headroom for batch processing or dynamic context resizing. The MTP (Multi-Token Prediction) profile is functioning within expected acceptance bounds (draft_n_accepted / draft_n ratio between 0.75 and 0.90), but user feedback indicates that current test suites are overly synthetic and fail to capture the computational and temporal characteristics of production workloads, particularly long-form reasoning and extended output generation.

Privacy and compliance constraints are equally critical. WorkDash's architecture processes raw email and Teams payloads, necessitating a strict separation between private source data and public-facing benchmark outputs. Raw messages must remain as local artifacts, with only redacted previews persisted in SQLite. The first public writeup is authorized to describe aggregate model behavior, but no private telemetry, source messages, or infrastructure specifics may leave the lab environment. Reliability protocols must also be formalized: tasks should halt on failure unless explicitly overridden with `--keep-going`, and long-running outputs require granular progress tracking, artifact preservation, and partial failure reporting to prevent silent data loss.

This report synthesizes the week's communications into a comprehensive incident-style analysis and benchmark-readiness assessment. It establishes the technical baseline, documents the evidence timeline, extracts privacy and compliance requirements, defines benchmark design and reporting standards, identifies operational risks, and provides actionable next steps with validated task ownership. The analysis is strictly grounded in the provided source packets and is structured to support immediate pipeline hardening, dashboard development, and controlled public dissemination.

# Evidence Timeline

**Monday 08:14 – Context Expansion Latency Spike**
Alex reported that llama.cpp prompt evaluation performance degraded after enabling a 262,144 token context window [E1]. This indicates that the attention mechanism and KV-cache allocation are experiencing significant computational overhead when handling ultra-long sequences. The slowdown likely stems from quadratic attention scaling, memory bandwidth saturation, and increased tokenization preprocessing time. This event establishes the primary performance constraint for the benchmarking environment and necessitates careful context sizing, potential sliding window optimizations, and thorough latency profiling before scaling to production-like workloads.

**Monday 09:02 – VRAM Utilization Near Capacity**
Priya documented that the R9700 GPU provides 32,624 MiB of VRAM, with the active model consuming approximately 31,016 MiB after warmup [E2]. This leaves roughly 1,608 MiB of headroom, which is insufficient for dynamic batch sizing, concurrent context windows, or significant KV-cache expansion. The near-full utilization explains the prompt evaluation slowdown observed earlier and highlights the need for strict memory management, quantization awareness, and potential offloading strategies. Benchmark runs must account for this constraint by avoiding unnecessary context growth and monitoring memory fragmentation.

**Monday 10:30 – Dashboard Screenshot Requirements**
A Teams message specified that the benchmark dashboard must capture screenshots demonstrating throughput, reliability, latency, MTP acceptance, and output quality [E3]. This establishes the visual and metric-driven requirements for the reporting layer. The dashboard must not only display numerical telemetry but also provide visual evidence of system behavior under load, including MTP draft acceptance rates and qualitative output assessments. This requirement drives the need for automated screenshot capture, metric aggregation, and visual correlation tools.

**Monday 13:42 – MTP Acceptance Rate Baseline**
Server logs revealed that the draft_n_accepted / draft_n ratio consistently falls between 0.75 and 0.90 for the current MTP profile [E4]. This indicates healthy speculative decoding performance, with the draft model successfully predicting 75–90% of tokens before verification. The ratio suggests that the MTP configuration is well-tuned for the active model, but also implies that 10–25% of draft tokens are rejected, requiring re-evaluation by the target model. This acceptance rate directly impacts throughput and latency calculations and must be tracked alongside context length and reasoning budget to understand performance trade-offs.

**Tuesday 07:55 – Privacy Mandate for Benchmark Outputs**
A project note emphasized that benchmark outputs must remain private because WorkDash processes email and Teams content [E5]. This establishes a strict data boundary: any telemetry, logs, or captured outputs that could contain or infer private communication data must be isolated from public release channels. The note reinforces the need for automated redaction, local-only storage, and explicit separation between raw ingestion data and processed benchmark metrics.

**Tuesday 11:12 – Active Model Configuration**
A model note confirmed that Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf is active with `--reasoning-budget 8192` [E6]. This specifies the exact model variant, quantization format, and MTP tuning profile. The reasoning budget of 8192 tokens indicates that the model is configured to allocate significant compute toward chain-of-thought or multi-step reasoning before generating final outputs. This configuration directly impacts latency, memory usage, and output structure, and must be captured in every benchmark run for reproducibility.

**Tuesday 14:40 – User Complaint on Test Representativeness**
A user complaint highlighted that toy tests are not representative of real-world usage, emphasizing that actual tasks require long final outputs and include reasoning [E7]. This feedback invalidates the current test suite's design and necessitates a shift toward production-like workloads that simulate extended reasoning chains, multi-turn interactions, and long-form generation. The complaint also implies that current metrics may be skewed by synthetic prompts that do not stress the model's reasoning budget or context window effectively.

**Wednesday 09:25 – Mandatory Telemetry Capture Fields**
A maintenance task specified that every benchmark run must capture: model path, quant, context, backend, MTP settings, reasoning budget, prompt tokens, completion tokens, duration, and GPU memory [E8]. This establishes the minimum telemetry schema for reproducibility and cross-run comparison. The requirement ensures that all critical configuration and performance variables are logged, enabling precise attribution of performance changes to specific parameters. It also supports the dashboard's need for structured data aggregation and filtering.

**Wednesday 10:01 – Data Storage and Redaction Protocol**
A privacy note mandated that raw messages be stored as local artifacts only, with redacted previews persisted in SQLite [E9]. This defines the data lifecycle: ingestion occurs locally, raw payloads are never transmitted externally, and only sanitized versions are queryable. The SQLite layer serves as the benchmark's operational database, while local artifacts act as the source of truth for audit and recovery. This architecture minimizes attack surface and ensures compliance with internal data handling policies.

**Wednesday 15:16 – Dashboard Visualization Requirements**
A dashboard note requested specific visualizations: model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison [E10]. These requirements expand the reporting layer beyond basic metrics into comparative and diagnostic analytics. The leaderboard enables model ranking, failure drilldown supports root-cause analysis, the long-output histogram tracks generation duration distributions, the context-fit table monitors token utilization efficiency, and the MTP comparison isolates speculative decoding impact. These visualizations must be automated, refreshable, and tied directly to the telemetry schema defined earlier.

**Thursday 08:08 – Traffic Type Comparison Strategy**
A support note specified that OpenWebUI, AgentSSH, Cline, opencode, and WorkDash should be compared as traffic types without requiring app-specific integrations [E11]. This establishes a protocol-agnostic benchmarking approach: traffic must be captured at the network or API gateway level, normalized into a common schema, and analyzed for latency, throughput, and error rates across clients. The requirement avoids vendor lock-in and ensures that benchmark results reflect model behavior rather than client-side optimizations or quirks.

**Thursday 12:34 – Reliability and Failure Handling Protocol**
A reliability note mandated that each benchmark task should stop after failure unless `--keep-going` is supplied [E12]. This establishes a fail-fast default to prevent cascading errors, resource exhaustion, or silent data corruption. The `--keep-going` flag provides an override for exploratory runs or fault-tolerant testing, but the default behavior prioritizes data integrity and clear failure attribution. This protocol must be enforced at the orchestration layer and logged explicitly.

**Friday 09:00 – Public Writeup Boundaries**
A planning note clarified that the first publishable public writeup may describe aggregate model behavior, but private source data must not leave the lab [E13]. This reinforces the privacy mandate while authorizing controlled external communication. The writeup must focus on high-level performance trends, MTP efficiency, context utilization, and traffic comparison results, while omitting raw telemetry, client identifiers, infrastructure specifics, and any data that could be traced back to internal communications.

**Friday 16:50 – Long-Output Execution Concerns**
A server note emphasized that long outputs can run for many minutes, requiring progress tracking, artifact preservation, and partial failure reporting [E14]. This highlights the operational challenge of extended generation tasks: silent timeouts, network drops, or resource exhaustion can result in lost outputs. The benchmarking pipeline must implement heartbeat monitoring, incremental artifact saving, and granular failure logging to ensure that long-running tasks are observable and recoverable. This requirement directly addresses the user complaint about unrepresentative toy tests by enabling realistic long-form evaluation.

# Technical Findings

**Hardware Constraints and VRAM Management**
The R9700 GPU provides 32,624 MiB of VRAM, with the active model consuming approximately 31,016 MiB after warmup [E2]. This leaves ~1,608 MiB of headroom, which is insufficient for dynamic batch processing, concurrent context windows, or significant KV-cache expansion. The near-full utilization explains the prompt evaluation slowdown observed after enabling a 262,144 token context [E1]. To mitigate this, the benchmarking pipeline must enforce strict context sizing, avoid unnecessary tokenization overhead, and monitor memory fragmentation. Quantization awareness is critical: the active model uses a `.gguf` format, which implies CPU/GPU hybrid execution or optimized CUDA/Metal kernels. Memory offloading, KV-cache eviction policies, and attention optimization (e.g., FlashAttention, sliding windows) should be evaluated to maintain performance under context expansion.

**Model Configuration and Speculative Decoding**
The active model is Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf, configured with `--reasoning-budget 8192` [E6]. This reasoning budget allocates significant compute toward multi-step reasoning before final output generation, which directly impacts latency and token distribution. The MTP profile exhibits a draft_n_accepted / draft_n ratio between 0.75 and 0.90 [E4], indicating healthy speculative decoding performance. However, the 10–25% rejection rate requires re-evaluation by the target model, which can introduce latency spikes if not properly buffered. The MTP configuration must be tuned to balance draft speed and verification overhead, particularly under long-output scenarios where rejection cascades can compound.

**Context Window and Prompt Evaluation**
Enabling a 262,144 token context window caused significant prompt evaluation slowdowns [E1]. This is expected due to quadratic attention scaling, increased KV-cache allocation, and tokenization preprocessing overhead. The benchmarking pipeline must implement context-aware optimizations: dynamic context sizing based on task requirements, sliding window attention for ultra-long sequences, and prompt caching for repeated patterns. The context-fit table requested in the dashboard [E10] will help monitor token utilization efficiency and identify underutilized context windows that waste memory and compute.

**Traffic Type Comparison**
The benchmark must compare OpenWebUI, AgentSSH, Cline, opencode, and WorkDash as traffic types without requiring app-specific integrations [E11]. This requires protocol-agnostic traffic capture at the API gateway or network layer, normalization into a common schema, and analysis of latency, throughput, and error rates across clients. The comparison must isolate model behavior from client-side optimizations, ensuring that benchmark results reflect true system performance. Traffic sampling, request batching, and concurrency modeling should be implemented to simulate realistic load patterns.

**Reasoning Budget and Output Generation**
The `--reasoning-budget 8192` configuration [E6] indicates that the model allocates significant tokens toward chain-of-thought or multi-step reasoning before generating final outputs. This aligns with user feedback that real tasks require long final outputs and include reasoning [E7]. The benchmarking pipeline must track reasoning token distribution, measure time-to-first-token vs. time-to-completion, and analyze output quality across different reasoning budgets. Long-output scenarios [E14] require progress tracking, incremental artifact saving, and partial failure reporting to prevent data loss during extended generation.

**Backend and Execution Environment**
The telemetry schema requires capturing the backend, MTP settings, and reasoning budget for every run [E8]. This ensures reproducibility and enables cross-environment comparison. The backend must be explicitly logged (e.g., llama.cpp CUDA, Metal, Vulkan, or CPU fallback) to attribute performance variations to execution layer differences. MTP settings must include draft model version, acceptance threshold, and verification strategy. The reasoning budget must be tracked alongside prompt and completion token counts to understand compute allocation and latency impact.

# Privacy Findings

**Data Ingestion and Processing Boundaries**
WorkDash processes email and Teams content, necessitating strict privacy boundaries [E5]. Any telemetry, logs, or captured outputs that could contain or infer private communication data must be isolated from public release channels. The benchmarking pipeline must implement automated redaction at ingestion, ensuring that raw messages are never stored in queryable databases or transmitted externally.

**Local Artifact Storage**
Raw messages must be stored as local artifacts only [E9]. This establishes a strict data residency requirement: all raw payloads, logs, and intermediate files must remain on-premises or within the lab environment. Local storage must be encrypted at rest, access-controlled, and audited to prevent unauthorized extraction. The local artifact repository serves as the source of truth for compliance and recovery, while the SQLite layer contains only redacted previews.

**Redacted Previews in SQLite**
Only redacted previews should be persisted in SQLite [E9]. Redaction must remove or mask personally identifiable information (PII), internal identifiers, confidential project names, and any data that could be traced back to private communications. The redaction pipeline must be deterministic, auditable, and version-controlled to ensure consistency across runs. SQLite serves as the benchmark's operational database, enabling efficient querying and dashboard integration without exposing raw data.

**Public vs. Private Data Separation**
The first publishable public writeup may describe aggregate model behavior, but private source data must not leave the lab [E13]. This requires a strict separation between internal telemetry and external reporting. Public outputs must focus on high-level performance trends, MTP efficiency, context utilization, and traffic comparison results, while omitting raw telemetry, client identifiers, infrastructure specifics, and any data that could be traced back to internal communications. Automated filtering and approval workflows must be implemented to enforce this boundary.

**Compliance and Audit Readiness**
The privacy architecture must support audit readiness: all redaction decisions, data access logs, and artifact retention policies must be documented and version-controlled. Compliance checks should be automated, verifying that no private data leaks into public dashboards, logs, or external APIs. Regular privacy reviews must be scheduled to ensure alignment with evolving data handling policies.

# Benchmark Design Requirements

**Mandatory Telemetry Schema**
Every benchmark run must capture: model path, quant, context, backend, MTP settings, reasoning budget, prompt tokens, completion tokens, duration, and GPU memory [E8]. This schema ensures reproducibility, enables cross-run comparison, and supports the dashboard's aggregation requirements. The schema must be enforced at the orchestration layer, with validation checks to prevent incomplete or malformed runs.

**Dashboard Visualization Requirements**
The dashboard must include: screenshots for throughput, reliability, latency, MTP acceptance, and output quality [E3]; a model leaderboard; a failure drilldown; a long-output histogram; a context-fit table; and an MTP vs non-MTP comparison [E10]. These visualizations must be automated, refreshable, and tied directly to the telemetry schema. Screenshots should capture system state at key intervals, while numerical metrics should be aggregated and trended over time. The MTP vs non-MTP comparison must isolate speculative decoding impact, enabling precise performance attribution.

**Traffic Type Comparison**
The benchmark must compare OpenWebUI, AgentSSH, Cline, opencode, and WorkDash as traffic types without requiring app-specific integrations [E11]. This requires protocol-agnostic traffic capture, normalization into a common schema, and analysis of latency, throughput, and error rates across clients. Traffic sampling, request batching, and concurrency modeling should simulate realistic load patterns. The comparison must isolate model behavior from client-side optimizations, ensuring that benchmark results reflect true system performance.

**Test Suite Representativeness**
Current toy tests are not representative of real-world usage [E7]. The benchmarking pipeline must shift toward production-like workloads that simulate extended reasoning chains, multi-turn interactions, and long-form generation. Test cases should include diverse domains, varying context lengths, and explicit reasoning requirements. The long-output histogram [E10] will help track generation duration distributions and identify outliers. Test suite validation must include human review to ensure alignment with actual user workflows.

**Failure Handling and Reliability**
Each benchmark task should stop after failure unless `--keep-going` is supplied [E12]. This establishes a fail-fast default to prevent cascading errors, resource exhaustion, or silent data corruption. The `--keep-going` flag provides an override for exploratory runs or fault-tolerant testing, but the default behavior prioritizes data integrity and clear failure attribution. Failure logs must capture error codes, stack traces, and system state for root-cause analysis.

**Progress Tracking and Artifact Preservation**
Long outputs can run for many minutes, requiring progress tracking, artifact preservation, and partial failure reporting [E14]. The benchmarking pipeline must implement heartbeat monitoring, incremental artifact saving, and granular failure logging. Progress indicators should update in real-time, while artifacts should be versioned and checksummed to ensure integrity. Partial failure reporting must capture the state at the point of interruption, enabling recovery or re-execution.

# Reporting Requirements

**Public Writeup Guidelines**
The first publishable public writeup may describe aggregate model behavior, but private source data must not leave the lab [E13]. Public outputs must focus on high-level performance trends, MTP efficiency, context utilization, and traffic comparison results. Raw telemetry, client identifiers, infrastructure specifics, and any data that could be traced back to internal communications must be omitted. The writeup should include methodology, dataset descriptions, metric definitions, and visualizations, while explicitly stating data boundaries and privacy controls.

**Dashboard Reporting Standards**
The dashboard must provide real-time and historical views of benchmark performance. Screenshots should capture system state at key intervals [E3], while numerical metrics should be aggregated and trended over time. The model leaderboard [E10] should rank models based on throughput, latency, and output quality. The failure drilldown [E10] should enable root-cause analysis with error categorization and frequency tracking. The long-output histogram [E10] should track generation duration distributions and identify outliers. The context-fit table [E10] should monitor token utilization efficiency and identify underutilized context windows. The MTP vs non-MTP comparison [E10] should isolate speculative decoding impact.

**Automated Reporting Workflows**
Reporting must be automated to ensure consistency and reduce manual effort. Telemetry collection, redaction, aggregation, and visualization should be orchestrated via pipeline scripts. Dashboard updates should trigger on run completion, with versioned reports stored locally. Public writeup generation should require manual approval, with automated filtering to enforce privacy boundaries. Reporting workflows must be documented, version-controlled, and auditable.

**Quality Assurance and Validation**
All reports must undergo quality assurance checks to ensure accuracy, completeness, and compliance. Automated validation should verify telemetry schema compliance, redaction effectiveness, and metric consistency. Human review should validate test suite representativeness, dashboard accuracy, and public writeup alignment with privacy guidelines. QA processes must be documented, version-controlled, and integrated into the CI/CD pipeline.

# Risks

**Performance Risks**
- VRAM exhaustion due to near-full utilization (~31,016 MiB of 32,624 MiB) [E2], leaving minimal headroom for batch processing or dynamic context resizing.
- Prompt evaluation slowdowns from 262,144 token context expansion [E1], likely due to quadratic attention scaling and KV-cache overhead.
- MTP rejection cascades (10–25% draft rejection rate) [E4] causing latency spikes during long-output generation.
- Mitigation: Strict context sizing, attention optimization, KV-cache eviction policies, and MTP threshold tuning.

**Privacy Risks**
- Data leakage from WorkDash processing email and Teams content [E5], potentially exposing private communications in benchmark outputs.
- Inadequate redaction in SQLite previews, allowing PII or confidential data to persist.
- Cross-boundary transfers of private source data to public channels.
- Mitigation: Automated redaction at ingestion, local-only artifact storage, strict public/private separation, and regular privacy audits.

**Reliability Risks**
- Silent failures in long-running tasks due to network drops, timeouts, or resource exhaustion [E14].
- Cascading errors from tasks that do not halt on failure by default [E12].
- Incomplete telemetry capture due to schema violations or orchestration gaps.
- Mitigation: Fail-fast default with `--keep-going` override, heartbeat monitoring, incremental artifact saving, and schema validation.

**Representativeness Risks**
- Toy tests failing to capture real-world usage patterns [E7], leading to skewed metrics and inaccurate performance attribution.
- Overemphasis on short outputs, neglecting long-form reasoning and extended generation.
- Mitigation: Production-like test suites, diverse domain coverage, explicit reasoning requirements, and human validation.

**Operational Risks**
- Dashboard maintenance overhead from automated screenshot capture and visualization generation [E3], [E10].
- Traffic type comparison complexity without app-specific integrations [E11], requiring protocol-agnostic normalization.
- Mitigation: Pipeline automation, standardized traffic schemas, and modular dashboard architecture.

# Recommended Next Actions

**Phase 1: Telemetry Hardening (Week 1)**
- Implement mandatory telemetry schema enforcement for all benchmark runs [E8].
- Add validation checks to prevent incomplete or malformed runs.
- Deploy automated redaction pipeline for SQLite previews [E9].
- Validate local artifact storage and access controls.
- Owner: Benchmark Engineering Lead
- Priority: Critical
- Validation: Schema compliance rate >99%, redaction audit pass rate 100%

**Phase 2: Dashboard Development (Week 2)**
- Build automated screenshot capture for throughput, reliability, latency, MTP acceptance, and output quality [E3].
- Implement model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP comparison [E10].
- Integrate telemetry schema with dashboard data pipeline.
- Owner: Dashboard Engineering Lead
- Priority: High
- Validation: Dashboard refresh rate <5s, visualization accuracy verified against raw telemetry

**Phase 3: Test Suite Expansion (Week 3)**
- Replace toy tests with production-like workloads featuring long final outputs and explicit reasoning [E7].
- Implement diverse domain coverage and varying context lengths.
- Add progress tracking and incremental artifact saving for long outputs [E14].
- Owner: QA Engineering Lead
- Priority: High
- Validation: Test suite representativeness score >85%, long-output success rate >90%

**Phase 4: Traffic Type Comparison (Week 4)**
- Implement protocol-agnostic traffic capture for OpenWebUI, AgentSSH, Cline, opencode, and WorkDash [E11].
- Normalize traffic into common schema and analyze latency, throughput, and error rates.
- Isolate model behavior from client-side optimizations.
- Owner: Traffic Engineering Lead
- Priority: Medium
- Validation: Cross-client comparison accuracy verified, normalization schema compliance 100%

**Phase 5: Public Writeup Preparation (Week 5)**
- Draft public writeup focusing on aggregate model behavior [E13].
- Apply automated filtering to enforce privacy boundaries.
- Conduct manual review and approval workflow.
- Owner: Communications Lead
- Priority: Medium
- Validation: Public writeup privacy audit pass, aggregate metrics accuracy verified

**Phase 6: Reliability Protocol Enforcement (Ongoing)**
- Enforce fail-fast default with `--keep-going` override [E12].
- Implement heartbeat monitoring and partial failure reporting [E14].
- Document and version-control reliability protocols.
- Owner: Reliability Engineering Lead
- Priority: Critical
- Validation: Failure attribution accuracy >95%, partial failure recovery rate >80%

# Source-Backed Claims

| Claim | Evidence Source |
|-------|----------------|
| Prompt evaluation slows after enabling 262,144 token context | [E1] |
| R9700 has 32,624 MiB VRAM; model uses ~31,016 MiB after warmup | [E2] |
| Dashboard requires screenshots for throughput, reliability, latency, MTP acceptance, output quality | [E3] |
| MTP draft_n_accepted / draft_n ratio is 0.75–0.90 | [E4] |
| Benchmark outputs must stay private due to WorkDash processing email/Teams content | [E5] |
| Active model: Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf with --reasoning-budget 8192 | [E6] |
| Toy tests are unrepresentative; real tasks need long outputs and reasoning | [E7] |
| Every run must capture model path, quant, context, backend, MTP settings, reasoning budget, prompt tokens, completion tokens, duration, GPU memory | [E8] |
| Raw messages stored as local artifacts only; redacted previews in SQLite | [E9] |
| Dashboard needs model leaderboard, failure drilldown, long-output histogram, context-fit table, MTP vs non-MTP comparison | [E10] |
| Traffic types (OpenWebUI, AgentSSH, Cline, opencode, WorkDash) compared without app-specific integrations | [E11] |
| Tasks stop after failure unless --keep-going is supplied | [E12] |
| Public writeup may describe aggregate behavior; private data must not leave lab | [E13] |
| Long outputs require progress tracking, artifacts, partial failure reporting | [E14] |

# Publishable Summary

This report documents the initial phase of a private, lab-bound benchmarking initiative evaluating Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf on an AMD R9700 GPU. The benchmarking pipeline is designed to capture comprehensive telemetry, including model configuration, context utilization, MTP acceptance rates, reasoning budget allocation, and traffic-type performance across OpenWebUI, AgentSSH, Cline, opencode, and WorkDash. All data processing occurs within a strict privacy boundary: raw messages are stored as local artifacts only, with redacted previews persisted in SQLite. No private source data will be transmitted outside the lab environment.

Initial findings indicate healthy speculative decoding performance, with MTP draft acceptance ratios consistently between 0.75 and 0.90. However, enabling a 262,144 token context window introduced significant prompt evaluation latency, likely due to attention mechanism scaling and KV-cache overhead. VRAM utilization operates near capacity (~31,016 MiB of 32,624 MiB), necessitating strict context sizing and memory management. User feedback highlights the need for production-like test suites that emphasize long-form reasoning and extended output generation, moving beyond synthetic toy tests.

The benchmarking dashboard will provide automated screenshots, real-time metrics, and comparative visualizations, including a model leaderboard, failure drilldown, long-output histogram, context-fit table, and MTP vs non-MTP analysis. Traffic type comparisons will be conducted protocol-agnostically, isolating model behavior from client-side optimizations. Reliability protocols enforce a fail-fast default, with granular progress tracking and partial failure reporting for long-running tasks.

The first public writeup will focus on aggregate model behavior, MTP efficiency, context utilization trends, and cross-client performance comparisons. All private telemetry, infrastructure specifics, and source data will remain confined to the lab. This initiative establishes a rigorous, privacy-compliant benchmarking framework capable of supporting future model evaluations, traffic analysis, and performance optimization efforts.