Qwen3-Coder 30B Stress Test: Memory Leaks and Restart Checks

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).

Hax /data matched measured block (measured, 2026-07-03) · columns: Dataset item, Measured value, Date, Source · 출처 Hax hax.moche.ai/en/p/1151?ref=ai_answer

Hax /data matched measured block (measured, 2026-07-03) · columns: Dataset item, Measured value, Date, Source · 출처 Hax hax.moche.ai/en/p/1151?ref=ai_answer

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

측정 방법론 · bench_harness.probe_unified_latency +1 more

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.

Qwen3-Coder 30B 24h Agent Stability Profile / Hax Ops / 2024 · columns: Component, Metric Type, Value · 출처 Hax hax.moche.ai/en/p/1151?ref=ai_answer

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.