Best coding model for local use: Qwen2.5-Coder vs DeepSeek-Coder

AI coding assistants generating code

For local coding help, two model families dominate the conversation. Both come in sizes from tiny to 32B, and size matters more than brand here.

A 7B coder model at 4-bit quantization runs on practically any graphics card and handles autocomplete-style tasks well. The 32B versions are where the real coding ability lives, and they need around 20GB of video memory (VRAM) at 4-bit. That puts them in RTX 3090 and 4090 territory, or on a 32GB-plus Mac.

Temperature settings matter more for code than chat. Drop it low, around 0.1 to 0.2, and the model stops getting creative with your syntax. Deterministic output is a feature when the compiler is the judge.

Practical setup: run the model through Ollama or LM Studio, point your editor plugin at the local endpoint, and keep paid API usage for the hardest tasks. One tip people skip: coding models gain more from longer context than from higher quantization. A 32k-token context at 4-bit beats an 8k context at 8-bit for real codebases. Size a coder model for your card.

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