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

Fast verdict

qwen2.5-coder:32b runs at 38.9 tok/s on a 24GB RTX 3090 — in the deliberate range. This model prioritizes quality or parameter count over raw speed. Test it on offline or background tasks first, and consider a smaller quantization if interactive response time matters.

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 #11 of 18 in throughput among currently measured models on this RTX 3090. The next faster model is translategemma:27b (41.3 tok/s, 6% faster). The next slower model is qwen3.6:35b (34.6 tok/s, 12% 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: deliberate
• RTX 3090 speed: 38.9 tok/s
• Latency: 3061 ms
• Test time: 2026-07-01T06:49:08Z
• 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

• qwen3-coder:30b: 157.6 tok/s | latency 853 ms | test 2026-07-01T06:49:08Z
• gpt-oss:20b: 156.1 tok/s | latency 1524 ms | test 2026-04-29T05:39:58Z
• qwen3:8b: 136.4 tok/s | latency 1281 ms | test 2026-07-01T06:49:08Z
• ministral-3:14b: 88.1 tok/s | latency 1860 ms | test 2026-07-01T06:49:08Z
• qwen2.5:14b: 84.0 tok/s | latency 946 ms | test 2026-04-29T05:39:58Z

RTX 3090 decision guide

1. Offline first: prioritize qwen2.5-coder:32b for scheduled batch inference, research, or validation workflows.
2. Context is the bottleneck: reduce context to the minimum viable length for your task.
3. Quantize before you buy hardware: Q4 or Q5 may make this viable on 24GB where Q8 is not.
4. Cloud for interactive: if real-time response is required, treat qwen2.5-coder:32b as a cloud-fallback candidate.

Comparisons to validate

• qwen2.5-coder:32b vs the next-fastest and next-slowest model in the benchmark feed.
• qwen2.5-coder:32b vs qwen3-coder:30b — same size tier, 39 vs 158 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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