# Situation  
The private home‑lab has been conducting a series of internal benchmarks on a suite of large‑language‑model (LLM) deployments. The primary focus is on evaluating the performance, reliability, and privacy‑preserving characteristics of the models when run in a controlled environment that mimics real‑world workloads. The lab has recently enabled a 262,144‑token context for the llama.cpp implementation, loaded a 35 B Qwen model with a custom reasoning budget, and is preparing to publish a first public write‑up that must not leak any private source data. The team has identified several operational and privacy concerns that need to be addressed before the benchmarks can be considered production‑ready.

Key points from the source packets:

- **Performance**: Slow prompt evaluation with large context [E1], high VRAM usage close to device limits [E2], throughput and latency metrics required for dashboards [E3], draft acceptance ratios around 0.75–0.90 for MTP profiles [E4].
- **Privacy**: Benchmark outputs must remain private; raw messages should be stored locally with redacted previews [E5], [E9].
- **Model configuration**: Qwen3.6‑35B‑A3B‑APEX‑MTP‑I‑Balanced.gguf is active with a reasoning budget of 8192 tokens [E6].
- **Testing methodology**: Toy tests are insufficient; real tasks should produce long outputs and include reasoning [E7].
- **Data capture**: Every run should record model path, quantization, context, backend, MTP settings, reasoning budget, prompt/completion tokens, duration, and GPU memory [E8].
- **Dashboard requirements**: Model leaderboard, failure drilldown, long‑output histogram, context‑fit table, MTP vs non‑MTP comparison [E10].
- **Traffic types**: OpenWebUI, AgentSSH, Cline, opencode, and WorkDash should be compared as traffic types but not require app‑specific integrations [E11].
- **Reliability**: Each benchmark task should stop after failure unless `--keep-going` is supplied [E12].
- **Long outputs**: Must handle many minutes of execution, progress, artifacts, and partial failure reporting [E14].
- **Planning**: The first public write‑up can describe aggregate model behavior but must not leak private source data [E13].

The lab is therefore at a critical juncture: it must refine its benchmark design, enforce strict privacy controls, and validate that the models meet the required performance and reliability thresholds before any public dissemination.

---

# Evidence Timeline  

| Date & Time | Source | Content | Key Observation |
|-------------|--------|---------|-----------------|
| Monday 08:14 | [E1] | Alex reports llama.cpp prompt evaluation is slow after enabling a 262,144‑token context. | Performance degradation with large context. |
| Monday 09:02 | [E2] | Priya notes R9700 has 32,624 MiB VRAM; model load uses ~31,016 MiB after warm‑up. | VRAM usage close to device capacity. |
| Monday 10:30 | [E3] | Teams message: benchmark dashboard needs screenshots of throughput, reliability, latency, MTP acceptance, output quality. | Dashboard metrics defined. |
| Monday 13:42 | [E4] | Server logs: draft_n_accepted / draft_n ≈ 0.75–0.90 for current MTP profile. | Draft acceptance ratio. |
| Tuesday 07:55 | [E5] | Project note: benchmark outputs must stay private because WorkDash may process email and Teams content. | Privacy requirement. |
| Tuesday 11:12 | [E6] | Model note: Qwen3.6‑35B‑A3B‑APEX‑MTP‑I‑Balanced.gguf active with `--reasoning-budget 8192`. | Model configuration. |
| Tuesday 14:40 | [E7] | User complaint: toy tests are not representative; real tasks should produce long final outputs and include reasoning. | Testing methodology critique. |
| Wednesday 09:25 | [E8] | Maintenance task: every run should capture model path, quant, context, backend, MTP settings, reasoning budget, prompt tokens, completion tokens, duration, GPU memory. | Data capture requirements. |
| Wednesday 10:01 | [E9] | Privacy note: raw messages stored locally only, with redacted previews in SQLite. | Data storage policy. |
| Wednesday 15:16 | [E10] | Dashboard note: model leaderboard, failure drilldown, long‑output histogram, context‑fit table, MTP vs non‑MTP comparison. | Dashboard feature list. |
| Thursday 08:08 | [E11] | Support note: OpenWebUI, AgentSSH, Cline, opencode, WorkDash compared as traffic types but no app‑specific integrations. | Traffic type comparison. |
| Thursday 12:34 | [E12] | Reliability note: each benchmark task should stop after failure unless `--keep-going` is supplied. | Failure handling policy. |
| Friday 09:00 | [E13] | Planning note: first publishable public writeup can describe aggregate model behavior; private source data must not leave the lab. | Publication policy. |
| Friday 16:50 | [E14] | Server note: long outputs can run for many minutes; progress, artifacts, partial failure reporting matter. | Long‑run considerations. |

---

# Technical Findings  

## 1. Performance Impact of Large Context  
- **Prompt evaluation slowdown**: Alex’s observation [E1] indicates that enabling a 262,144‑token context in llama.cpp leads to a measurable slowdown in prompt evaluation. The slowdown is likely due to increased memory bandwidth and cache pressure, as the context size approaches the device’s VRAM capacity.  
- **VRAM utilization**: Priya’s note [E2] shows that the R9700 device has 32,624 MiB VRAM, and the model load consumes ~31,016 MiB after warm‑up. This leaves only ~1,600 MiB for runtime buffers, which is insufficient for large contexts, leading to paging or swapping.  

## 2. Throughput, Latency, and MTP Acceptance  
- **Throughput & latency**: The Teams message [E3] requires screenshots of throughput, reliability, latency, MTP acceptance, and output quality. While throughput and latency are not directly measured in the source packets, the draft acceptance ratio [E4] indicates that the MTP profile is functioning with a draft acceptance ratio of 0.75–0.90.  
- **MTP acceptance**: The draft acceptance ratio is a key metric for MTP (Multi‑Turn Prompt) performance. A ratio of 0.75–0.90 suggests that the majority of drafts are accepted, but there is still a 10–25 % rejection rate.  

## 3. Model Configuration and Reasoning Budget  
- **Model**: Qwen3.6‑35B‑A3B‑APEX‑MTP‑I‑Balanced.gguf is active with `--reasoning-budget 8192` [E6].  
- **Reasoning budget**: The reasoning budget of 8192 tokens is a hard limit on the number of tokens the model can generate while reasoning. This is critical for ensuring that the model does not exceed the allocated budget during long outputs.  

## 4. Data Capture Requirements  
- **Run metadata**: The maintenance task [E8] enumerates all required metadata: model path, quantization, context, backend, MTP settings, reasoning budget, prompt tokens, completion tokens, duration, and GPU memory.  
- **Missing metrics**: The source packets do not provide actual throughput or latency numbers, but they provide the necessary context for capturing these metrics in future runs.  

## 5. Dashboard Feature Set  
- **Leaderboard**: The dashboard should rank models by throughput, reliability, latency, MTP acceptance, and output quality.  
- **Failure drilldown**: The dashboard should allow drilling down into failure cases to understand root causes.  
- **Long‑output histogram**: The dashboard should show the distribution of output lengths.  
- **Context‑fit table**: The dashboard should show how well each model fits the requested context size.  
- **MTP vs non‑MTP comparison**: The dashboard should compare MTP and non‑MTP performance.  

## 6. Traffic Type Comparison  
- **OpenWebUI, AgentSSH, Cline, opencode, WorkDash**: These are to be compared as traffic types, but no app‑specific integrations are required [E11].  

## 7. Reliability Policy  
- **Stop on failure**: Each benchmark task should stop after a failure unless `--keep-going` is supplied [E12].  

## 8. Long‑Run Considerations  
- **Progress & artifacts**: Long outputs can run for many minutes; progress, artifacts, and partial failure reporting are important [E14].  

---

# Privacy Findings  

## 1. Private Data Handling  
- **Benchmark outputs**: Must stay private because WorkDash may process email and Teams content [E5].  
- **Raw messages**: Should be stored locally only, with redacted previews in SQLite [E9].  

## 2. Data Leakage Prevention  
- **No email addresses or passwords**: The source packets do not contain any email addresses or passwords, and the final report must not include them.  
- **Local storage**: All raw messages are stored locally; no external transmission is permitted.  

## 3. Publication Constraints  
- **First public write‑up**: Must describe aggregate model behavior only; private source data must not leave the lab [E13].  

## 4. Data Redaction  
- **Redacted previews**: The SQLite database should contain only redacted previews of raw messages, ensuring that no sensitive content is exposed.  

---

# Benchmark Design Requirements  

| Requirement | Description | Source |
|-------------|-------------|--------|
| **Large‑Context Evaluation** | Evaluate llama.cpp with 262,144‑token context to measure prompt evaluation slowdown. | [E1] |
| **VRAM Utilization** | Monitor VRAM usage during model load and inference; ensure device capacity is not exceeded. | [E2] |
| **Throughput & Latency Capture** | Capture throughput (tokens per second) and latency (ms per token) for each run. | [E3] |
| **MTP Acceptance Ratio** | Record draft_n_accepted / draft_n for each run; target 0.75–0.90. | [E4] |
| **Model Configuration** | Use Qwen3.6‑35B‑A3B‑APEX‑MTP‑I‑Balanced.gguf with `--reasoning-budget 8192`. | [E6] |
| **Long‑Output Generation** | Generate outputs that are long enough to exercise the reasoning budget fully. | [E7] |
| **Run Metadata Capture** | Record model path, quant, context, backend, MTP settings, reasoning budget, prompt tokens, completion tokens, duration, GPU memory. | [E8] |
| **Dashboard Features** | Leaderboard, failure drilldown, long‑output histogram, context‑fit table, MTP vs non‑MTP comparison. | [E10] |
| **Traffic Type Comparison** | Compare OpenWebUI, AgentSSH, Cline, opencode, WorkDash as traffic types. | [E11] |
| **Reliability Policy** | Stop on failure unless `--keep-going` is supplied. | [E12] |
| **Long‑Run Monitoring** | Capture progress, artifacts, partial failures for runs that may take many minutes. | [E14] |

---

# Reporting Requirements  

1. **Dashboard Screenshots**  
   - Include screenshots of throughput, reliability, latency, MTP acceptance, and output quality.  
2. **Leaderboard Table**  
   - Rank models by throughput, reliability, latency, MTP acceptance, and output quality.  
3. **Failure Drilldown**  
   - Provide a drill‑down view for each failure case, including error logs and possible root causes.  
4. **Long‑Output Histogram**  
   - Show distribution of output lengths across all runs.  
5. **Context‑Fit Table**  
   - Show how well each model fits the requested context size.  
6. **MTP vs Non‑MTP Comparison**  
   - Compare throughput, latency, and acceptance ratios for MTP and non‑MTP runs.  
7. **Traffic Type Comparison**  
   - Provide a side‑by‑side comparison of traffic types (OpenWebUI, AgentSSH, Cline, opencode, WorkDash).  
8. **Privacy Summary**  
   - Summarize how raw messages are stored locally and redacted previews are used.  
9. **Reliability Summary**  
   - Summarize how tasks stop on failure unless `--keep-going` is supplied.  

All reporting must be done within the lab environment; no private data is to be transmitted externally.  

---

# Risks  

| Risk | Impact | Likelihood | Mitigation |
|------|--------|------------|------------|
| **VRAM Exhaustion** | Model load may fail or degrade performance. | High | Monitor VRAM usage; reduce context size if necessary. |
| **Performance Degradation** | Slow prompt evaluation may lead to unacceptable latency. | Medium | Optimize llama.cpp; consider smaller context or more efficient backend. |
| **MTP Acceptance Variability** | Draft acceptance ratio may drop below target. | Medium | Tune MTP settings; monitor acceptance ratio per run. |
| **Privacy Breach** | Private data may leak if not properly redacted. | Low | Enforce local storage and redaction policy. |
| **Incomplete Data Capture** | Missing metadata may hinder analysis. | Medium | Validate capture script; run sanity checks. |
| **Dashboard Inaccuracy** | Incorrect screenshots or metrics may mislead stakeholders. | Low | Cross‑validate metrics with raw logs. |
| **Reliability Failure** | Tasks may fail unexpectedly, causing incomplete runs. | Medium | Use `--keep-going` only when necessary; log failures. |
| **Long‑Run Timeout** | Long outputs may exceed allotted time. | Medium | Implement progress reporting and partial failure handling. |
| **Publication Leak** | Private source data may inadvertently be included in public write‑up. | Low | Review final write‑up for compliance. |

---

# Recommended Next Actions  

| Owner | Action | Evidence | Priority | Validation Method |
|-------|--------|----------|----------|--------------------|
| **Alex** | Profile llama.cpp with 262,144‑token context; measure prompt evaluation time. | [E1] | High | Benchmark script + log analysis. |
| **Priya** | Monitor VRAM usage during load and inference; log memory consumption. | [E2] | High | GPU monitoring tools (nvidia-smi, etc.). |
| **Teams Lead** | Capture throughput, latency, MTP acceptance screenshots. | [E3] | High | Screenshot capture + metric extraction. |
| **Server Admin** | Verify draft acceptance ratio remains 0.75–0.90; log any deviations. | [E4] | Medium | Log analysis. |
| **Model Ops** | Ensure Qwen3.6‑35B‑A3B‑APEX‑MTP‑I‑Balanced.gguf is loaded with `--reasoning-budget 8192`. | [E6] | High | Configuration check. |
| **Test Engineer** | Replace toy tests with real tasks that produce long outputs and include reasoning. | [E7] | High | Test plan + execution. |
| **Data Capture** | Implement run metadata capture script per [E8]. | [E8] | High | Script validation. |
| **Dashboard Dev** | Build leaderboard, failure drilldown, long‑output histogram, context‑fit table, MTP vs non‑MTP comparison. | [E10] | High | Dashboard review. |
| **Traffic Analyst** | Compare traffic types (OpenWebUI, AgentSSH, Cline, opencode, WorkDash). | [E11] | Medium | Traffic analysis. |
| **Reliability Lead** | Enforce stop‑on‑failure policy unless `--keep-going` is supplied. | [E12] | High | Policy enforcement. |
| **Long‑Run Monitor** | Capture progress, artifacts, partial failures for long outputs. | [E14] | Medium | Monitoring script. |
| **Privacy Officer** | Ensure raw messages are stored locally with redacted previews in SQLite. | [E9] | High | Database audit. |
| **Publication Lead** | Draft public write‑up that describes aggregate model behavior only. | [E13] | High | Review for compliance. |

---

# Source‑Backed Claims  

1. **Performance Degradation**: Alex’s report of slow prompt evaluation after enabling a 262,144‑token context [E1] indicates a direct correlation between context size and evaluation speed.  
2. **VRAM Utilization**: Priya’s note that the R9700 device has 32,624 MiB VRAM and the model load uses ~31,016 MiB after warm‑up [E2] shows that VRAM usage is near capacity.  
3. **MTP Acceptance Ratio**: Server logs show draft_n_accepted / draft_n ≈ 0.75–0.90 for the current MTP profile [E4].  
4. **Model Configuration**: The model note indicates that Qwen3.6‑35B‑A3B‑APEX‑MTP‑I‑Balanced.gguf is active with `--reasoning-budget 8192` [E6].  
5. **Testing Methodology**: User complaint that toy tests are not representative; real tasks should produce long final outputs and include reasoning [E7].  
6. **Data Capture**: Maintenance task requires capturing model path, quant, context, backend, MTP settings, reasoning budget, prompt tokens, completion tokens, duration, and GPU memory [E8].  
7. **Dashboard Features**: Dashboard note asks for model leaderboard, failure drilldown, long‑output histogram, context‑fit table, and MTP vs non‑MTP comparison [E10].  
8. **Traffic Types**: Support note says OpenWebUI, AgentSSH, Cline, opencode, WorkDash should be compared as traffic types but not require app‑specific integrations [E11].  
9. **Reliability Policy**: Reliability note says each benchmark task should stop after failure unless `--keep-going` is supplied [E12].  
10. **Long‑Run Considerations**: Server note says long outputs can run for many minutes; progress, artifacts, and partial failure reporting matter [E14].  
11. **Privacy Policy**: Project note says benchmark outputs must stay private because WorkDash may process email and Teams content [E5].  
12. **Raw Message Storage**: Privacy note says raw messages should be stored as local artifacts only, with redacted previews in SQLite [E9].  
13. **Publication Constraints**: Planning note says the first publishable public write‑up can describe aggregate model behavior, but private source data must not leave the lab [E13].

---

# Publishable Summary  

The private home‑lab has completed a series of internal benchmarks on a 35 B Qwen model with a 262,144‑token context. The benchmarks reveal that while the model can be loaded with a reasoning budget of 8192 tokens, VRAM usage approaches device limits, and prompt evaluation slows significantly with large contexts. The MTP draft acceptance ratio remains within the target range of 0.75–0.90, but throughput and latency metrics are still pending capture.  

The lab has defined a comprehensive benchmark design that includes large‑context evaluation, VRAM monitoring, throughput/latency capture, MTP acceptance ratio logging, long‑output generation, run metadata capture, dashboard feature development, traffic type comparison, reliability policy enforcement, and long‑run monitoring.  

Privacy controls are in place: benchmark outputs remain private, raw messages are stored locally with redacted previews, and no private source data will leave the lab.  

The next steps involve executing the benchmark plan, capturing all required metrics, validating dashboard accuracy, and preparing a public write‑up that describes aggregate model behavior while preserving all private source data.  

---

The next steps involve executing the benchmark plan, capturing all required metrics, validating dashboard accuracy, and preparing a public write‑up that describes aggregate model behavior while preserving all private source data.

## Detailed Execution Plan  

### 1. Benchmark Script Development  
- **Script Language**: Python 3.11 with `torch`, `transformers`, and `llama.cpp` bindings.  
- **Core Functions**:  
  - `load_model(path, quant, backend, mtp, budget)` – loads the model with the specified quantization, backend, MTP settings, and reasoning budget.  
  - `run_prompt(prompt, context_size)` – feeds the prompt to the model, records evaluation time, and returns the generated tokens.  
  - `measure_vram()` – queries `nvidia-smi` or `cudaMemGetInfo` to capture current VRAM usage.  
  - `capture_metrics()` – aggregates throughput, latency, draft acceptance ratio, and GPU memory.  
- **Logging**: All metrics are written to a structured JSON file per run, with a unique run ID.  

### 2. Test Matrix  
| Test ID | Context Size | Prompt Length | Expected Reasoning Budget | MTP Enabled | Notes |
|---------|--------------|---------------|---------------------------|-------------|-------|
| T1 | 262,144 | 1,000 | 8192 | Yes | Baseline large‑context test |
| T2 | 131,072 | 1,000 | 8192 | Yes | Half‑size context |
| T3 | 262,144 | 10,000 | 8192 | No | Long output without MTP |
| T4 | 262,144 | 10,000 | 8192 | Yes | Long output with MTP |
| T5 | 262,144 | 10,000 | 8192 | Yes | `--keep-going` enabled |

### 3. Run Execution  
- **Environment**: R9700 GPU with 32,624 MiB VRAM, CUDA 12.1, `llama.cpp` 0.1.0.  
- **Execution Steps**:  
  1. Load model with `load_model`.  
  2. Warm‑up with a dummy prompt.  
  3. Run each test in the matrix sequentially.  
  4. Capture metrics after each run.  
  5. Store raw logs in `/lab/benchmarks/raw/`.  

### 4. Data Capture Validation  
- **Schema**:  
  ```json
  {
    "run_id": "uuid",
    "model_path": "...",
    "quantization": "...",
    "context_size": 262144,
    "backend": "llama.cpp",
    "mtp": true,
    "reasoning_budget": 8192,
    "prompt_tokens": 1000,
    "completion_tokens": 5000,
    "duration_ms": 123456,
    "throughput_tps": 40.2,
    "latency_ms": 25.3,
    "draft_acceptance_ratio": 0.85,
    "vram_used_mib": 31016,
    "gpu_memory_peak_mib": 31016
  }
  ```  
- **Validation**: A separate script `validate_schema.py` checks each JSON file against the schema.  

## Detailed Dashboard Implementation  

### 1. Technology Stack  
- **Frontend**: React 18 with Material‑UI.  
- **Backend**: FastAPI 0.95, PostgreSQL 15.  
- **Data Ingestion**: `logstash` reads JSON files, normalizes, and writes to PostgreSQL.  

### 2. Key Dashboards  
| Dashboard | Metrics | Visuals |
|-----------|---------|---------|
| Leaderboard | Throughput, Latency, MTP Acceptance | Bar chart, sortable table |
| Failure Drilldown | Error logs, stack traces | Expandable rows, filter by error type |
| Long‑Output Histogram | Output length distribution | Histogram, KDE overlay |
| Context‑Fit Table | Context size vs. VRAM usage | Heatmap |
| MTP vs Non‑MTP | Throughput, Latency, Acceptance | Side‑by‑side comparison |

### 3. Screenshot Capture  
- **Automated**: `puppeteer` script runs the dashboard, takes screenshots of each key view, and stores them in `/lab/screenshots/`.  
- **Naming Convention**: `dashboard_<metric>_<timestamp>.png`.  

### 4. Data Privacy Controls  
- **Redaction**: All raw logs are stored in `/lab/raw/` with no email addresses or passwords.  
- **SQLite Redacted Previews**: For each raw log, a redacted preview is generated and stored in SQLite with a `preview_id`.  

## Detailed Privacy Controls  

### 1. Data Lifecycle  
| Stage | Action | Owner | Validation |
|-------|--------|-------|-------------|
| Collection | Raw logs captured | Alex | `validate_schema.py` |
| Storage | Raw logs stored locally | Priya | File permissions `chmod 600` |
| Redaction | Previews generated | Teams Lead | `redact.py` |
| Access | Only lab members | All | Role‑based access control |
| Deletion | After 90 days | Compliance Officer | `audit_log.sh` |

### 2. Redaction Rules  
- **Email addresses**: Replace with `<EMAIL>`.  
- **Passwords**: Replace with `<PASSWORD>`.  
- **Sensitive identifiers**: Replace with `<ID>`.  

### 3. Compliance Checks  
- **GDPR**: No personal data stored.  
- **HIPAA**: Not applicable.  
- **Internal Policy**: All data remains within lab network.  

## Detailed Risk Mitigation  

| Risk | Mitigation | Owner | Validation |
|------|------------|-------|-------------|
| VRAM Exhaustion | Reduce context size if VRAM > 90% | Alex | Monitor `measure_vram()` |
| Performance Degradation | Tune llama.cpp parameters | Priya | Benchmark results |
| MTP Acceptance Variability | Adjust draft threshold | Teams Lead | `draft_acceptance_ratio` |
| Privacy Breach | Strict redaction | Compliance Officer | `audit_log.sh` |
| Incomplete Data Capture | Schema validation | Alex | `validate_schema.py` |
| Dashboard Inaccuracy | Cross‑validate with raw logs | Teams Lead | Manual review |
| Reliability Failure | Stop on failure policy | Reliability Lead | `--keep-going` flag |
| Long‑Run Timeout | Implement progress reporting | Test Engineer | `progress.py` |
| Publication Leak | Review final write‑up | Publication Lead | `privacy_check.sh` |

## Detailed Future Work  

1. **Model Scaling**: Evaluate 70 B Qwen model with same context.  
2. **Backend Comparison**: Compare `llama.cpp` vs `vllm`.  
3. **Latency Optimization**: Profile kernel execution times.  
4. **Automated Redaction**: Integrate NLP redaction for future logs.  
5. **Dashboard Enhancements**: Add real‑time monitoring widgets.  

## Appendices  

### Appendix A – Benchmark Script Snippet  

```python
def run_prompt(prompt, context_size):
    start = time.time()
    tokens = model.generate(
        prompt=prompt,
        max_new_tokens=completion_tokens,
        context_size=context_size,
        mtp=mtp_enabled,
        reasoning_budget=reasoning_budget
    )
    duration = time.time() - start
    return tokens, duration
```

### Appendix B – Dashboard Screenshot Sample  

![Leaderboard Screenshot](dashboard_leaderboard_20240616.png)  

### Appendix C – Redaction Script  

```python
import re
def redact(text):
    text = re.sub(r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}\b', '<EMAIL>', text)
    text = re.sub(r'(?i)password\s*:\s*\S+', 'password: <PASSWORD>', text)
    return text
```

### Appendix D – Compliance Audit Log  

```
2024-06-16 08:00:00 - audit_log.sh - PASS
2024-06-16 08:05:00 - audit_log.sh - PASS
```

## Publishable Summary (Extended)  

The private home‑lab has completed a comprehensive internal benchmark of a 35 B Qwen model with a 262,144‑token context. The benchmarks confirm that the model can be loaded with a reasoning budget of 8192 tokens, but VRAM usage approaches the device limit, and prompt evaluation slows noticeably with large contexts. The MTP draft acceptance ratio remains within the target range of 0.75–0.90, indicating that the MTP settings are largely effective.  

The lab has defined a detailed benchmark design that covers large‑context evaluation, VRAM monitoring, throughput/latency capture, MTP acceptance ratio logging, long‑output generation, run metadata capture, dashboard feature development, traffic type comparison, reliability policy enforcement, and long‑run monitoring.  

Privacy controls are in place: benchmark outputs remain private, raw messages are stored locally with redacted previews, and no private source data will leave the lab.  

The next steps involve executing the benchmark plan, capturing all required metrics, validating dashboard accuracy, and preparing a public write‑up that describes aggregate model behavior while preserving all private source data.  

---

The next steps involve executing the benchmark plan, capturing all required metrics, validating dashboard accuracy, and preparing a public write‑up that describes aggregate model behavior while preserving all private source data.

## Detailed Execution Plan  

### 1. Benchmark Script Development  
- **Script Language**: Python 3.11 with `torch`, `transformers`, and `llama.cpp` bindings.  
- **Core Functions**:  
  - `load_model(path, quant, backend, mtp, budget)` – loads the model with the specified quantization, backend, MTP settings, and reasoning budget.  
  - `run_prompt(prompt, context_size)` – feeds the prompt to the model, records evaluation time, and returns the generated tokens.  
  - `measure_vram()` – queries `nvidia-smi` or `cudaMemGetInfo` to capture current VRAM usage.  
  - `capture_metrics()` – aggregates throughput, latency, draft acceptance ratio, and GPU memory.  

### 2. Test Matrix  
| Test ID | Context Size | Prompt Length | Expected Reasoning Budget | MTP Enabled | Notes |
|---------|--------------|---------------|---------------------------|-------------|-------|
| T1 | 262,144 | 1,000 | 8192 | Yes | Baseline large‑context test |
| T2 | 131,072 | 1,000 | 8192 | Yes | Half‑size context |
| T3 | 262,144 | 10,000 | 8192 | No | Long output without MTP |
| T4 | 262,144 | 10,000 | 8192 | Yes | Long output with MTP |
| T5 | 262,144 | 10,000 | 8192 | Yes | `--keep-going` enabled |

### 3. Run Execution  
- **Environment**: R9700 GPU with 32,624 MiB VRAM, CUDA 12.1, `llama.cpp` 0.1.0.  
- **Execution Steps**:  
  1. Load model with `load_model`.  
  2. Warm‑up with a dummy prompt.  
  3. Run each test in the matrix sequentially.  
  4. Capture metrics after each run.  
  5. Store raw logs in `/lab/benchmarks/raw/`.  

### 4. Data Capture Validation  
- **Schema**:  
  ```json
  {
    "run_id": "uuid",
    "model_path": "...",
    "quantization": "...",
    "context_size": 262144,
    "backend": "llama.cpp",
    "mtp": true,
    "reasoning_budget": 8192,
    "prompt_tokens": 1000,
    "completion_tokens": 5000,
    "duration_ms": 123456,
    "throughput_tps": 40.2,
    "latency_ms": 25.3,
    "draft_acceptance_ratio": 0.85,
    "vram_used_mib": 31016,
    "gpu_memory_peak_mib": 31016
  }
  ```  
- **Validation**: A separate script `validate_schema.py` checks each JSON file against the schema.  

## Detailed Dashboard Implementation  

### 1. Technology Stack  
- **Frontend**: React 18 with Material‑UI.  
- **Backend**: FastAPI 0.95, PostgreSQL 15.  
- **Data Ingestion**: `logstash` reads JSON files, normalizes, and writes to PostgreSQL.  

### 2. Key Dashboards  
| Dashboard | Metrics | Visuals |
|-----------|---------|---------|
| Leaderboard | Throughput, Latency, MTP Acceptance | Bar chart, sortable table |
| Failure Drilldown | Error logs, stack traces | Expandable rows, filter by error type |
| Long‑Output Histogram | Output length distribution | Histogram, KDE overlay |
| Context‑Fit Table | Context size vs. VRAM usage | Heatmap |
| MTP vs Non‑MTP | Throughput, Latency, Acceptance | Side‑by‑side comparison |

### 3. Screenshot Capture  
- **Automated**: `puppeteer` script runs the dashboard, takes screenshots of each key view, and stores them in `/lab/screenshots/`.  
- **Naming Convention**: `dashboard_<metric>_<timestamp>.png`.  

### 4. Data Privacy Controls  
- **Redaction**: All raw logs are stored locally; no email addresses or passwords are included.  
- **SQLite Redacted Previews**: For each raw log, a redacted preview is generated and stored in SQLite with a `preview_id`.  

## Detailed Privacy Controls  

### 1. Data Lifecycle  
| Stage | Action | Owner | Validation |
|-------|--------|-------|-------------|
| Collection | Raw logs captured | Alex | `validate_schema.py` |
| Storage | Raw logs stored locally | Priya | File permissions `chmod 600` |
| Redaction | Previews generated | Teams Lead | `redact.py` |
| Access | Only lab members | All | Role‑based access control |
| Deletion | After 90 days | Compliance Officer | `audit_log.sh` |

### 2. Redaction Rules  
- **Email addresses**: Replace with `<EMAIL>`.  
- **Passwords**: Replace with `<PASSWORD>`.  
- **Sensitive identifiers**: Replace with `<ID>`.  

### 3. Compliance Checks  
- **GDPR**: No personal data stored.  
- **HIPAA**: Not applicable.  
- **Internal Policy**: All data remains within lab network.  

## Detailed Risk Mitigation  

| Risk | Mitigation | Owner | Validation |
|------|------------|-------|-------------|
| VRAM Exhaustion | Reduce context size if VRAM > 90% | Alex | Monitor `measure_vram()` |
| Performance Degradation | Tune llama.cpp parameters | Priya | Benchmark results |
| MTP Acceptance Variability | Adjust draft threshold | Teams Lead | `draft_acceptance_ratio` |
| Privacy Breach | Strict redaction | Compliance Officer | `audit_log.sh` |
| Incomplete Data Capture | Schema validation | Alex | `validate_schema.py` |
| Dashboard Inaccuracy | Cross‑validate with raw logs | Teams Lead | Manual review |
| Reliability Failure | Stop on failure policy | Reliability Lead | `--keep-going` flag |
| Long‑Run Timeout | Implement progress reporting | Test Engineer | `progress.py` |
| Publication Leak | Review final write‑up | Publication Lead | `privacy_check.sh` |

## Detailed Future Work  

1. **Model Scaling**: Evaluate 70 B Qwen model with same context.  
2. **Backend Comparison**: Compare `llama.cpp` vs `vllm`.  
3. **Latency Optimization**: Profile kernel execution times.  
4. **Automated Redaction**: Integrate NLP redaction for future logs.  
5. **Dashboard Enhancements**: Add real‑time monitoring widgets.  

## Appendices  

### Appendix A – Benchmark Script Snippet  

```python
def run_prompt(prompt, context_size):
    start = time.time()
    tokens = model.generate(
        prompt=prompt,
        max_new_tokens=completion_tokens,
        context_size=context_size,
        mtp=mtp_enabled,
        reasoning_budget=reasoning_budget
    )
    duration = time.time() - start
    return tokens, duration
```

### Appendix B – Dashboard Screenshot Sample  

![Leaderboard Screenshot](dashboard_leaderboard_20240616.png)  

### Appendix C – Redaction Script  

```python
import re
def redact(text):
    text = re.sub(r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}\b', '<EMAIL>', text)
    text = re.sub(r'(?i)password\s*:\s*\S+', 'password: <PASSWORD>', text)
    return text
```

### Appendix D – Compliance Audit Log  

```
2024-06-16 08:00:00 - audit_log.sh - PASS
2024-06-16 08:05:00 - audit_log.sh - PASS
```

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