# M8 Capacity Baseline — 2026-02-09

**Mission 8:** Agent Bench Testing Systems  
**Hardware:** RTX PRO 6000 Blackwell (96GB VRAM)  
**Model:** Qwen2.5-Coder-32B-Instruct-AWQ  
**Test Date:** 2026-02-09 04:53 UTC

## Executive Summary

**No breaking point found at 20 concurrent users.** The single GPU handles 20+ simultaneous inference requests with 100% success rate and stable latency.

## Ramp Test Results

| Concurrent Users | Throughput | Avg Latency | P95 Latency | Success Rate |
|------------------|------------|-------------|-------------|--------------|
| 1 | 0.42 sess/sec | 2378ms | 3562ms | 100% |
| 5 | 2.11 sess/sec | 2230ms | 3869ms | 100% |
| 10 | 4.39 sess/sec | 2033ms | 3771ms | 100% |
| 15 | 6.22 sess/sec | 2171ms | 3911ms | 100% |
| 20 | 8.98 sess/sec | 1949ms | 3527ms | 100% |

## Key Findings

### Throughput Scaling
- Nearly linear scaling from 1→20 concurrent users
- ~9 sessions/second at 20 concurrent
- No signs of saturation at tested levels

### Latency Stability
- Average latency stays consistent (~2 seconds) regardless of load
- P95 latency stable at ~3.5-4 seconds
- No latency explosion under load

### Resource Usage
- VRAM: 91.6% avg (constant, model resident)
- GPU Utilization: 15-40% avg, peaks to 100%
- Temperature: 50°C (idle) → 70°C (20 concurrent)
- Safe thermal margin (throttle at ~83°C)

## Implications for M5 (Dream Server)

A single RTX PRO 6000 Blackwell can confidently support:
- **20+ concurrent LLM inference users**
- **~9 requests/second throughput**
- **Sub-4-second response times (p95)**

For a dual-GPU setup (.122 + .143):
- **40+ concurrent users** with load balancing
- **~18 requests/second aggregate throughput**

## Test Configuration

```json
{
  "scenarios": "generic-capacity.json",
  "concurrency_levels": [1, 5, 10, 15, 20],
  "duration_per_level": "15 seconds",
  "cooldown_between_levels": "5 seconds",
  "model": "Qwen/Qwen2.5-Coder-32B-Instruct-AWQ",
  "max_tokens": 150,
  "temperature": 0.7
}
```

## Next Steps

1. **Extended ramp test** — Try 25, 30, 40 concurrent to find actual breaking point
2. **Longer duration tests** — 5-minute sustained load at 20 concurrent
3. **Mixed workload** — Combine short and long prompts
4. **Dual-GPU test** — Load balance across .122 + .143

## Tools Used

- `tools/agent-bench/load_test.py` — Ramp testing with resource monitoring
- `tools/agent-bench/scenarios/generic-capacity.json` — Generic LLM capacity scenarios
- Cluster status API at `http://192.168.0.122:9199/status`

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*Generated by Android-17 (.122) — Mission 8 capacity testing*
