Coding LLM · 8GB VRAM

Best Coding LLM for 8GB VRAM (8GB VRAM)

The best local code models that fit an 8GB GPU

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

Short answer

The best local coding LLM for 8GB VRAM in 2026 is Qwen 2.5 Coder 7B — ~5GB at Q4_K_M, ~50 tokens/sec on an RTX 4060, and the strongest code completion in the 7B class. DeepSeek-Coder 6.7B is a close second for fill-in-the-middle, and it pairs perfectly with Continue.dev for a free local Copilot. 8GB runs any 7B coder at Q4; the 32B coders need 24GB.

Models that fit in 8GB VRAM

Tested reference: RTX 4060 / RTX 3070 (8GB). Figures are for Q4_K_M with a modest context window.

Model	Size	Build	VRAM used	Speed
WINNERQwen 2.5 Coder 7B Best local coder that fits 8GB — excellent completion + repo-level context. `ollama pull qwen2.5-coder:7b`	7B	Q4_K_M	~4.7GB	~50 tok/s
DeepSeek-Coder 6.7B Best fill-in-the-middle for autocomplete; great with Continue.dev. `ollama pull deepseek-coder:6.7b`	6.7B	Q4_K_M	~4.5GB	~52 tok/s
CodeLlama 7B Reliable, well-supported; good for older toolchains that expect Llama. `ollama pull codellama:7b`	7B	Q4_K_M	~4.5GB	~50 tok/s
Qwen 2.5 Coder 3B Fastest — near-instant inline autocomplete on an 8GB card. `ollama pull qwen2.5-coder:3b`	3B	Q4_K_M	~2.2GB	~95 tok/s

What won't fit in 8GB

✗Qwen 2.5 Coder 14B (needs ~9GB) — Needs 12GB — the noticeably smarter step up.
✗Codestral 22B (needs ~13GB) — Needs 16GB.
✗Qwen 2.5 Coder 32B (needs ~20GB) — The SOTA local coder — needs 24GB.

How to fit more in 8GB

→Use a small model (3B) for inline autocomplete and a 7B for chat/refactor — Continue.dev lets you assign different models per role.
→Keep the code context window ≤8K on 8GB; repo-wide context is what blows the VRAM budget.
→Q4_K_M keeps code accuracy high; avoid Q3 for coding — it introduces subtle syntax errors.

Quick start

Install Ollama

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

Run the winner

ollama run qwen2.5-coder:7b

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Frequently asked questions

What is the best local coding model for 8GB VRAM?

Qwen 2.5 Coder 7B — it fits in ~5GB at Q4, runs at ~50 tok/s, and leads the 7B class on code benchmarks. Pair it with Continue.dev in VS Code for a free, private Copilot.

Can I run a local Copilot on 8GB VRAM?

Yes. Run Qwen 2.5 Coder 7B (or DeepSeek-Coder 6.7B) in Ollama and connect Continue.dev in VS Code — you get autocomplete + chat entirely offline on an 8GB GPU.

Is a 7B coder good enough?

For autocomplete, boilerplate, tests and explanations — yes. For large multi-file refactors the 14B/32B coders are clearly better, but they need 12–24GB of VRAM.

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