Qwen3-Coder 30B Local Agent: Benchmarks, Time Savings, and Repetitive

Qwen3-Coder 30B is a large language model specifically optimized for code generation, refactoring, and debugging, designed to operate efficiently on local consumer hardware without cloud dependency. This model represents a significant shift in how developers handle repetitive coding tasks, offering a balance between computational cost and inference quality for mid-sized parameter models.

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/1154?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/1154?ref=ai_answer

Dataset item | Measured value | Date | Source
first_response_latency_ms | 119.2 ms | 2026-07-03 | bench_harness.probe_unified_latency
qwen-image(50스텝, 1024px, 콜드) 생성 시간 | 73 s | 2026-06-30 | Hax ComfyUI 풀 실측
발행 성공률 | 100.0 % | 2026-07-03 | Hax 운영 실측(telemetry/funnel)

측정 방법론 · bench_harness.probe_unified_latency +1 more

표본

2 measured metrics (Hax /data curated)

측정 환경

RTX PRO 6000 Blackwell ×4 풀; ComfyUI 0.24.0

수집일

2026-06-30 ~ 2026-07-03

방법

bench_harness.probe_unified_latency; 1장 콜드 스타트

How can you reproduce these numbers?#

Follow the source column above and our open dataset at /data.

Qwen3-Coder 30B Performance Metrics (Hax Evaluation / General Benchmarks) · columns: Metric, Hax Status, Estimated Value · 출처 Hax hax.moche.ai/en/p/1154?ref=ai_answer

Qwen3-Coder 30B Performance Metrics (Hax Evaluation / General Benchmarks) · columns: Metric, Hax Status, Estimated Value · 출처 Hax hax.moche.ai/en/p/1154?ref=ai_answer

Metric | Hax Status | Estimated Value
Edit Success Rate | Not Measured / 측정대기 | ~75% (Est. on HumanEval)
Compile Pass Rate | Not Measured / 측정대기 | ~60% (Est. on MBPP)
Token Generation Speed | Not Measured / 측정대기 | ~40-60 tok/s (Est. on RTX 4070)
Context Window Utilization | Not Measured / 측정대기 | ~128k tokens (Spec)

The table above reflects the current state of localized testing. Since specific measured data for the Qwen3-Coder 30B variant in a controlled local environment is not available for this report, all performance figures are labeled as estimates based on community benchmarks and architectural projections. Users must distinguish between cloud-hosted theoretical maxima and local hardware constraints.

How does the 30B parameter size impact daily workflow?#

The 30B parameter size places the model in a critical 'sweet spot' for local deployment. It is large enough to understand complex code structures and multi-file contexts but small enough to run on consumer-grade GPUs like the NVIDIA RTX 4070 or 4080 with 12GB to 16GB of VRAM, often using 4-bit or 8-bit quantization. This allows developers to run the model entirely offline, ensuring that proprietary code never leaves the local machine. For repetitive tasks such as generating unit tests, writing documentation, or refactoring legacy code, the model provides immediate feedback loops that are faster than waiting for cloud API responses.

Can it truly replace repetitive manual coding tasks?#

For boilerplate generation, the estimated time savings are significant. Tasks that previously required 15-30 minutes of manual typing and lookup can be completed in seconds. However, the 'edit success rate' remains an estimated figure, hovering around 75% for standard tasks. This means human review is still required. The model excels at providing a strong first draft but may struggle with highly specific, non-standard library integrations without additional context. The 'compile pass rate' is estimated at approximately 60%, indicating that while syntactically correct, semantic logic errors may persist.

Note: All performance metrics cited as 'Estimated' are based on aggregated community reports and should not be treated as guaranteed SLAs. Local hardware configurations vary widely, impacting actual throughput.