Today's Local LLM Pick: qwen2.5-coder:32b on RTX 3090 (2026)

Fast verdict

qwen2.5-coder:32b is a heavy model on 24GB VRAM (2.9 tok/s). It is best suited for offline batch processing, proof-of-concept validation, or cloud fallback scenarios. Reduce context or step down quantization before attempting interactive use.

qwen2.5-coder:32b approaches the 24GB boundary at higher quantizations. Consider Q4 or Q5 if you need context headroom on the RTX 3090. It ranks #16 of 18 in throughput among currently measured models on this RTX 3090. The next faster model is qwen3.5:122b (4.9 tok/s, 68% faster). The next slower model is qwen3.5:35b (2.8 tok/s, 6% slower).

The daily goal is simple: help a 3090 owner decide what to download tonight, what to skip, and when a cloud fallback is the better use of time.

Today’s pick

• Model: qwen2.5-coder:32b
• Category: coding
• Size tier: large
• Performance tier: heavy
• RTX 3090 speed: 2.9 tok/s
• Latency: 33306 ms
• Test time: 2026-09-09T07:21:06Z
• Baseline command:

ollama run qwen2.5-coder:32b

Who should try it

• Developers evaluating qwen2.5-coder:32b for code completion, refactoring, or agentic coding on a local RTX 3090.
• Teams that want a private, offline coding assistant without sending source code to a cloud API.
• Anyone comparing qwen2.5-coder:32b against Copilot or cloud coding agents on latency and throughput.

Who should skip it

• Users whose primary workload is long-context chat or document analysis rather than code.
• Teams that need guaranteed performance on a specific programming language; test with your own benchmark first.
• 8GB/12GB GPU owners unless a smaller quantized variant is available.

Watch points

• Output quality varies by language: test qwen2.5-coder:32b on your primary language before depending on it.
• Temperature sensitivity: coding tasks usually perform best at temperature 0; higher values may introduce errors.
• Context window: verify the model keeps instruction adherence stable at the context length you need.

Verified benchmark anchors

• gpt-oss:20b: 156.1 tok/s | latency 1524 ms | test 2026-04-29T05:39:58Z
• qwen3-coder:30b: 84.4 tok/s | latency 7842 ms | test 2026-09-09T07:21:06Z
• qwen2.5:14b: 84.0 tok/s | latency 946 ms | test 2026-04-29T05:39:58Z
• nemotron-3-nano:30b: 57.0 tok/s | latency 2468 ms | test 2026-04-01T11:53:50Z
• deepseek-r1:14b: 52.9 tok/s | latency 2724 ms | test 2026-09-09T07:21:06Z

RTX 3090 decision guide

1. Cloud may win: at 2.9 tok/s on 24GB, qwen2.5-coder:32b may be more cost-effective on RunPod or Vast.
2. Reduce aggressively: step down to Q4 or IQ4 and minimize context to fit VRAM.
3. Offline only: do not rely on this model for interactive or real-time local workloads.
4. Hardware path: if you run models this size daily, consider multi-GPU or cloud as a permanent solution.

Comparisons to validate

• qwen2.5-coder:32b vs the next-fastest and next-slowest model in the benchmark feed.
• qwen2.5-coder:32b vs gpt-oss:20b — same size tier, 3 vs 156 tok/s.
• qwen2.5-coder:32b local power cost vs A100 rental for the same workload.

Next actions

• Estimate VRAM fit: /en/tools/vram-calculator/
• Model page: /en/models/qwen25-coder-32b-q4/
• Benchmark changelog: /en/benchmarks/changelog/
• Local hardware path: /en/affiliate/hardware-upgrade/
• Cloud fallback: /go/runpod and /go/vast

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