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Local LLM Guide

Model

Qwen2.5-Coder 32B Instruct

Start with a supported compressed build and short context. 31.5GB RAM estimate. Best use: High-quality local coding, code repair, code reasoning, and programming-assistant workflows.

32.5BCode

Best use

High-quality local coding, code repair, code reasoning, and programming-assistant workflows. · Aider-style repair tasks and multi-file reasoning when hardware is sufficient.

Memory planning

31.5GB RAM estimate

Expected pace

Depends on your hardware, app, and conversation length

License

Apache-2.0; read the model’s terms before use

Who should use this model?

  • High-quality local coding, code repair, code reasoning, and programming-assistant workflows.
  • Aider-style repair tasks and multi-file reasoning when hardware is sufficient.
  • Developers who want the strongest local Qwen2.5 coding record .

Choose something else when

  • You have less than 32GB system RAM or limited disk bandwidth.
  • You need a lightweight always-on background coding assistant.
  • You want a general chat model more than a code-specialist model.

What hardware makes sense?

The download is only part of the memory needed. Leave room for the app, the input it processes, and other open programs. For chat models, context includes the instructions, conversation, and answer. Longer context needs more memory.

CPU only
Possible but slow
8GB system memory
Use a smaller model for this memory limit.
16GB system memory
Use a smaller model for this memory limit.
32GB system memory
A possible starting point with a compressed model and short conversation; check the exact file size.
8GB GPU memory
Use a smaller model for this memory limit.
12GB GPU memory
Use a smaller model for this memory limit.
24GB GPU memory
Memory or response time may limit use. Try a smaller model first.
Apple unified memory
Good 32GB plus

Model facts that change the decision

Parameters
32.5B
Input
Text
Published context limit
131,072 tokens
Model format
Safetensors · GGUF · Ollama
Suggested compression
Q4_K_M for broad local compatibility; Q5_K_M for better code quality if memory allows.
Typical Q4 file
19.9GB before runtime and context overhead
Languages
40+ programming languages

First setup

Ollama

ollama run qwen2.5-coder:32b

LM Studio: Search for `Qwen/Qwen2.5-Coder-32B-Instruct-GGUF` in LM Studio and select Q4_K_M or Q5_K_M.

llama.cpp

Use llama-server -hf Qwen/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M; for split GGUF downloads, download all shards for the chosen quantization and merge if your runtime requires a single file.

Conditions that change the answer

  • RAM and VRAM figures are estimates from GGUF file size plus conservative runtime and conversation memory overhead; exact memory depends on context length, batch size, backend, and GPU offload.
  • Official Qwen2.5-Coder cards state long-context support up to 128K, but GGUF cards and local runtimes commonly default to 32K unless configured otherwise.
  • Check that this exact model can use your app’s connected tools before relying on that feature.

Read the controlling license text →