Coding LLM · 16GB VRAM

Best Coding LLM for 16GB VRAM (16GB VRAM)

Local code models that shine on a 16GB GPU

📅 Published: July 7, 2026🔄 Last Updated: July 2026✓ Manually Reviewed

Short answer

The best local coding LLM for 16GB VRAM in 2026 is Qwen 2.5 Coder 14B — ~9GB at Q4_K_M, ~34 tokens/sec on an RTX 4080, and near-SOTA local code quality. Codestral 22B just fits and is excellent at fill-in-the-middle, while DeepSeek-Coder-V2 Lite 16B (MoE) is very fast for its quality. 16GB runs a 14B coder comfortably; the 32B coder needs 24GB.

Models that fit in 16GB VRAM

Tested reference: RTX 4080 / RTX 4060 Ti 16GB. Figures are for Q4_K_M with a modest context window.

Model	Size	Build	VRAM used	Speed
WINNERQwen 2.5 Coder 14B Best local coder that fits 16GB comfortably — strong multi-file reasoning. `ollama pull qwen2.5-coder:14b`	14B	Q4_K_M	~9.0GB	~34 tok/s
Codestral 22B Mistral’s code model — great FIM and 32K context; fits 16GB with short context. `ollama pull codestral`	22B	Q4_K_M	~13GB	~22 tok/s
DeepSeek-Coder-V2 Lite 16B MoE — fast because only 2.4B params are active per token. `ollama pull deepseek-coder-v2:16b`	16B (MoE, 2.4B active)	Q4_K_M	~10GB	~45 tok/s
Qwen 2.5 Coder 7B At 16GB you can run the 7B at Q5 for a small quality bump plus a long context. `ollama pull qwen2.5-coder:7b-instruct-q5_K_M`	7B	Q5_K_M	~5.5GB	~48 tok/s

What won't fit in 16GB

✗Qwen 2.5 Coder 32B (needs ~20GB) — The best local coder — needs 24GB.
✗DeepSeek-Coder-V2 236B (needs 130GB+) — Data-center class.

How to fit more in 16GB

→Run Qwen 2.5 Coder 14B as your chat/refactor model and a 3B coder for inline autocomplete — split roles in Continue.dev.
→Codestral fits 16GB only with an ≤8K context; for repo-wide context prefer the 14B.
→MoE models (DeepSeek-Coder-V2 Lite) give you 16B-class quality at 7B-class speed — a great 16GB pick.

Quick start

Install Ollama

curl -fsSL https://ollama.com/install.sh | sh

Run the winner

ollama run qwen2.5-coder:14b

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Frequently asked questions

Is 16GB VRAM enough for local coding AI?

Yes — 16GB runs Qwen 2.5 Coder 14B at Q4 (~9GB, ~34 tok/s) comfortably, plus a separate small autocomplete model. That covers a full local Copilot workflow.

Qwen 2.5 Coder 14B vs Codestral 22B on 16GB?

The 14B fits with room for a long context and is faster; Codestral 22B is strong but needs a short context to fit 16GB. For repo-wide work, pick the 14B.

Can 16GB run the 32B coder?

Not at usable quality — Qwen 2.5 Coder 32B needs ~20GB (24GB card). On 16GB, the 14B at Q4 is the right ceiling.

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