Who should use this model?
- Local coding assistant use on 16GB RAM machines.
- Code generation, code reasoning, and code repair at a small local footprint.
- CLI/editor helper experiments where a 32B code model is too heavy.
Choose something else when
- You need the strongest Qwen2.5-Coder checkpoint; use 32B if hardware permits.
- Your primary use is general creative chat rather than code.
- You only have 8GB RAM and need comfortable multitasking.
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
- A possible starting point with a compressed model and short conversation; check the exact file size.
- 8GB system memory
- Memory or response time may limit use. Try a smaller model first.
- 16GB system memory
- A possible starting point with a compressed model and short conversation; check the exact file size.
- 32GB system memory
- A possible starting point with a compressed model and short conversation; check the exact file size.
- 8GB GPU memory
- Memory or response time may limit use. Try a smaller model first.
- 12GB GPU memory
- A possible starting point with a compressed model and short conversation; check the exact file size.
- 24GB GPU memory
- A possible starting point with a compressed model and short conversation; check the exact file size.
- Apple unified memory
- Good 16GB plus
Model facts that change the decision
- Parameters
- 7.61B
- 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
- 4.68GB before runtime and context overhead
- Languages
- 40+ programming languages
First setup
Ollama
ollama run qwen2.5-coder:7bLM Studio: Search for `Qwen/Qwen2.5-Coder-7B-Instruct-GGUF` in LM Studio and select Q4_K_M or Q5_K_M.
llama.cpp
Use llama-server -hf Qwen/Qwen2.5-Coder-7B-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 →