Qwen3-Coder 30B Stress Test: Memory Leaks and Restart Checks
In short: Qwen3-Coder 30B is a large language model optimized for code generation and editing tasks, designed to assist developers in writing, debugging, and refactoring code through automated reasoning and pattern recognition. When deploying this model as a continuous coding agent, the critical failure modes are not low accuracy but infrastructure instability, specifically memory leaks and context window…
Qwen3-Coder 30B is a large language model optimized for code generation and editing tasks, designed to assist developers in writing, debugging, and refactoring code through automated reasoning and pattern recognition. When deploying this model as a continuous coding agent, the critical failure modes are not low accuracy but infrastructure instability, specifically memory leaks and context window exhaustion. A 24-hour stress test reveals whether the local inference stack can maintain state without crashing.
What did Hax measure on its own stack?#
Reference numbers Hax measured directly on its own infrastructure (measured, sourced).
| Dataset item | Measured value | Date | Source |
|---|---|---|---|
| 발행 성공률 | 100.0 % | 2026-07-03 | Hax 운영 실측(telemetry/funnel) |
| first_response_latency_ms | 119.2 ms | 2026-07-03 | bench_harness.probe_unified_latency |
| 생성 큐 성공률(누적 143건) | 77.6 % | 2026-06-30 | Hax ComfyUI 풀 운영 통계 |
- 표본
- 2 measured metrics (Hax /data curated)
- 수집일
- 2026-06-30 ~ 2026-07-03
- 방법
- bench_harness.probe_unified_latency; 누적 143건 중 성공 111(취소 21; 실패 11)
How can you reproduce these numbers?#
Follow the source column above and our open dataset at /data.
| Component | Metric Type | Value |
|---|---|---|
| GPU VRAM Peak | 측정 | Not Measured |
| RAM Leakage Rate | 추정 | 0.5% - 2% per 100k tokens |
| Edit Success Rate | 추정 | 65% - 75% on simple diffs |
| Compile Pass Rate | 추정 | 40% - 50% on complex logic |
| Context Overflow Freq | 추정 | High without pruning |
Why Does the Agent Crash After Hours?#
The primary cause of agent failure is VRAM and system RAM fragmentation. As the agent iterates, it accumulates conversation history, tool calls, and file states. Without aggressive context pruning, the KV cache grows linearly. In a 24-hour run, the estimated memory overhead can exceed the available VRAM, forcing the system to swap to slower RAM or crash entirely. This is not a model bug but a resource management issue.
How to Detect a Memory Leak?#
A memory leak is identified when the resident set size (RSS) of the inference process increases monotonically despite idle periods. In a healthy agent, memory usage should stabilize or drop after a task cycle. If the RSS continues to climb during idle time, the underlying framework or the application logic is failing to release allocated buffers. This often requires restarting the container or process to reclaim resources.
What Is a Realistic Edit Success Rate?#
The edit success rate measures how often the agent’s proposed code changes are syntactically correct and logically valid without human intervention. For Qwen3-Coder 30B, this is estimated between 65% and 75% for simple refactoring tasks. However, for complex architectural changes, the rate drops significantly. The compile pass rate is even lower, estimated at 40% to 50%, because compilation requires strict adherence to library versions and dependencies that the model may not fully track.
Note: These metrics are estimates based on typical local deployment scenarios. Actual performance depends heavily on hardware configuration and context management strategies. Always monitor system resources during long-running sessions.
Related reading: 터미널 AI 에이전트는 무엇이고, 왜 모델보다 스캐폴드가 중요한가?, 스스로 코딩하고 버그까지 고치는 AI, 오픈소스 OpenHands는 어떻게 동작하나?
References#
- Qwen Team - Qwen3-Coder Technical Report
- Hugging Face - Qwen3-Coder Documentation
- Ollama - Running Local LLMs
Responses
No responses yet. Be the first to respond.