Qwen 2.5 Coder 32B
By Alibaba · China
Updated 2026-07-13
Overview
Alibaba's Qwen 2.5 Coder 32B — the strongest open-weight code model we've benchmarked, trading punches with Claude 3.5 Sonnet on HumanEval.
When to pick this model
- Self-hosted code copilots replacing proprietary APIs
- Repo-scale analysis and refactoring up to 128k tokens
- Polyglot codebases spanning dozens of languages
- Commercial code tooling needing Apache 2.0
- Generating production code where quality justifies the VRAM
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 19 GB |
| Q5_K_M | 23 GB |
| Q8_0 | 35 GB |
| FP16 (no quantization) | 64 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Qwen 2.5 Coder 32B wants a 24 GB card at Q4_K_M (19 GB). Stepping up to Q8_0 nearly doubles the footprint to 35 GB, and unquantized FP16 weights take 64 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Qwen 2.5 Coder 32B needs roughly 32 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 3 tokens/sec on entry-level GPUs, on the order of 12 tokens/sec on a mid-range card, and up to 30 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Qwen 2.5 Coder 32B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Qwen 2.5 Coder 32B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 19 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 19 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Does not fit — needs 19 GB at Q4_K_M |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Q5_K_M (23 GB used) |
| 32 GB | RTX 5090 | Q5_K_M (23 GB used) |
Which GPU should you buy to run Qwen 2.5 Coder 32B?
To run Qwen 2.5 Coder 32B locally at Q4, you need ~19 GB of VRAM. The best value for this is a RTX 4090 (24 GB VRAM).
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Published benchmark scores
| Benchmark | Score |
|---|---|
| HumanEval | 92.7 |
| MBPP | 86 |
| LiveCodeBench | 31.4 |
Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.
To put Qwen 2.5 Coder 32B in context: its HumanEval score of 92.7 ranks #1 of the 26 catalog models with a published HumanEval result (catalog median 81.1). Scores are author-published and measured under different harnesses, so treat gaps of a few points as noise rather than a verdict.
Strengths
- Best-in-class open-weight code generation
- Claude 3.5 Sonnet-level HumanEval scores
- 128k context for repo-wide tasks
- Apache 2.0 license
Limitations
- Requires 20+ GB VRAM at Q4
- Weaker than Qwen 2.5 32B for general chat
- Slower than 7B-class models for autocomplete loops
Typical workloads
In our catalog grid, Qwen 2.5 Coder 32B is filed under Senior Dev, Refactor, Code Agents — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: code generation and review (pair it with an editor integration like Continue.dev or Cline).
The 128k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: Dense Transformer specialized for code · 64 layers
Training: General pre-training + 5.5T code tokens, 92 languages.
The default open-weight choice for serious code work — frontier-grade quality without an API bill.
Quick start
ollama run qwen2.5-coder:32bOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Qwen 2.5 Coder 32B need?
At the recommended Q4_K_M quantization, Qwen 2.5 Coder 32B needs about 19 GB of VRAM. Q8_0 takes 35 GB, and unquantized FP16 weights take 64 GB.
Can Qwen 2.5 Coder 32B run without a GPU?
Yes — with roughly 32 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Qwen 2.5 Coder 32B support?
Qwen 2.5 Coder 32B supports a 128k-token context window (131,072 tokens).
Can I use Qwen 2.5 Coder 32B commercially?
Yes. Qwen 2.5 Coder 32B is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is Qwen 2.5 Coder 32B on consumer hardware?
Our compatibility engine estimates on the order of 12 tokens/sec on a mid-range GPU and up to 30 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Qwen 2.5 Coder 32B should I download first?
Start with Q4_K_M (19 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. On a 24 GB card you can run up to Q5_K_M.