Offline Qwen3-Coder 30B: Local AI Coding Agent Benchmarks

Qwen3-Coder 30B is a large language model optimized for code generation, designed to function as a local coding agent that operates without internet connectivity by leveraging on-premise hardware inference. This architecture prioritizes data privacy and deterministic latency, allowing developers to execute complex coding tasks within isolated network environments where external API calls are prohibited or restricted. The success of such a system is defined by its ability to generate syntactically correct and logically sound code in the absence of real-time retrieval-augmented generation capabilities.

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/1150?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/1150?ref=ai_answer

Dataset item | Measured value | Date | Source
first_response_latency_ms | 119.2 ms | 2026-07-03 | bench_harness.probe_unified_latency
발행 성공률 | 100.0 % | 2026-07-03 | Hax 운영 실측(telemetry/funnel)
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.

Hax Offline Coding Benchmark (Local Environment, No Network) · columns: Model, Metric, Type · 출처 Hax hax.moche.ai/en/p/1150?ref=ai_answer

Hax Offline Coding Benchmark (Local Environment, No Network) · columns: Model, Metric, Type · 출처 Hax hax.moche.ai/en/p/1150?ref=ai_answer

Model | Metric | Type
Qwen3-Coder 30B | Pass@1 | 추정 68%
Qwen3-Coder 30B | Edit Success Rate | 측정 대기
Hax Reference Line | API Latency | 측정 0ms
Qwen2.5-Coder 32B | Pass@1 | 추정 64%

What defines offline success rate?#

The concept of offline success rate differs fundamentally from cloud-based benchmarks. In a cloud environment, models often rely on external documentation or search tools to verify syntax libraries. Offline, the model must rely entirely on its pre-trained weights. For a 30-billion parameter model like Qwen3-Coder 30B, this means the entire context of standard libraries (Python, JavaScript, Rust) must be retrieved from internal memory. The "edit success rate" measures whether the model can correctly modify existing codebases without introducing regressions, a task that is significantly harder than generating code from scratch. Without internet access, the model cannot correct hallucinations by checking live documentation, making the initial accuracy critical.

Is the compile pass rate reliable?#

Compile pass rate is a stringent metric that requires the generated code to not only look correct but to actually compile and run without errors. For Qwen3-Coder 30B, this metric is estimated based on standard datasets like HumanEval and MBPP, adapted for offline constraints. The model’s architecture allows for higher context window utilization, meaning it can understand larger code snippets before generating edits. However, without the ability to query external repositories for obscure library versions, the model may produce outdated syntax. The estimated pass rate reflects this limitation, showing high performance in standard tasks but potential drops in niche library usage.

The trade-off between speed and accuracy is evident in offline deployments. Users must ensure their hardware supports the model’s VRAM requirements to maintain low latency. If the model is offloaded to CPU, inference speeds drop significantly, affecting the interactive experience of a coding agent. The edit success rate, therefore, also depends on the hardware’s ability to process the model’s parameters in real-time.

Note: Benchmarks vary by hardware configuration and prompt engineering. Offline environments lack real-time verification, increasing the reliance on initial generation accuracy. The estimated success rates are based on standard evaluation sets and may not reflect specific proprietary codebases.