Qwen2.5-Coder 32B
Qwen 2.5 Coder 32B. Predecessor to Qwen3-Coder, still very capable.
Task Fit
Not marked for typed decisions in the current library.
Tool use, repo work, terminal workflows, and coding benchmarks.
Code generation, debugging, refactoring, and benchmark signal.
Not marked for chat in the current library.
Not marked for rag in the current library.
Not marked for vision in the current library.
Not marked for image generation in the current library.
Not marked for video generation in the current library.
Not marked for voice in the current library.
Source Confidence
Variants and Quant Artifacts
Choose the artifact first; hardware fit follows from RAM, VRAM, format, and runtime.
| Quant | Format | Quality | Min RAM | Reco RAM | Runtime | Action |
|---|---|---|---|---|---|---|
| Q4_K_M | gguf | balanced | 24GB | 48GB | ollama, llama.cpp, lm-studio | Plan with this |
| Q5_K_M | gguf | balanced | 24GB | 48GB | ollama, llama.cpp, lm-studio | Plan with this |
| Q8_0 | gguf | high | 37GB | 48GB | ollama, llama.cpp, lm-studio | Plan with this |
Recommended Hardware
Lowest estimated 5-year cost that can run this model.
Enough unified/system memory with a balanced 5-year cost.
Highest local performance signal among compatible hardware.
Benchmarks
Source and Review
Execution evidence
Run Qwen2.5-Coder 32B with a documented recipe
Recipes connect hardware, a model artifact, tools, settings, verification, and a reportable result.
No verified recipe is linked to this record yet.
Compatibility estimates remain available in the planner. A recipe appears here only after its exact stack and verification protocol are documented.
Similar Models
Qwen 2.5 VL (Vision-Language). Top open vision model.
Alibaba's flagship Qwen3. Competitive with GPT-4 class models.
Qwen3.8-2.4T-A95B is Alibaba Qwen's open-weight, text-only flagship MoE model with 2.4T total parameters and 95B activated parameters. It uses mandatory thinking, supports tool use, has a native 262K context window extensible to about 1.01M tokens, and ships in official BF16 and FP8 repositories. Its multi-terabyte weights target distributed data-center inference with vLLM, SGLang, or TokenSpeed rather than consumer local hardware.