Ollama · 12GB VRAM

Best Ollama Models for 12GB VRAM (12GB VRAM)

RTX 4070 / RTX 3060 12GB — the tier the 14B class was made for

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

Short answer

The best Ollama model for 12GB VRAM in 2026 is Phi-4 14B — ~9GB at Q4_K_M and ~25–32 tok/s on an RTX 3060 12GB / RTX 4070. 12GB is the 14B sweet spot: Qwen 3 14B and DeepSeek-R1 14B fit too, with Gemma 3 12B for multilingual work and Llama 3.1 8B as the fast lane.

Models that fit in 12GB VRAM

Tested reference: RTX 4070 / RTX 3060 12GB. Figures are for Q4_K_M with a modest context window.

Model	Size	Build	VRAM used	Speed
WINNERPhi-4 14B The 14B that punches hardest for its size — STEM/reasoning standout, with ~3GB left for context on 12GB. `ollama pull phi4`	14B	Q4_K_M	~9.0GB	~25–32 tok/s
Qwen 3 14B Best all-rounder of the 14B class (it takes the crown at 16GB, where its long context fits too). `ollama pull qwen3:14b`	14B	Q4_K_M	~9.0GB	~25–32 tok/s
DeepSeek-R1 14B Visible chain-of-thought — the best hard-problems pick that fits 12GB. `ollama pull deepseek-r1:14b`	14B	Q4_K_M	~9.0GB	~25–30 tok/s
Gemma 3 12B Best multilingual and writing quality, with a bit more KV-cache headroom than the 14Bs. `ollama pull gemma3:12b`	12B	Q4_K_M	~8.0GB	~28–35 tok/s
Llama 3.1 8B The speed pick — leaves ~7GB free, so a long context or a second small model fits alongside. `ollama pull llama3.1:8b`	8B	Q4_K_M	~5.0GB	~50–60 tok/s

What won't fit in 12GB

✗Codestral 22B (needs ~13GB) — Needs 16GB — on 12GB stick to Qwen 2.5 Coder 14B (~9GB) for code.
✗gpt-oss:20b (needs ~14–16GB) — OpenAI’s open-weight MoE is a hair too big — it is the headline pick one tier up.

How to fit more in 12GB

→12GB exists for the 14B class: ~9GB of weights plus 2–3GB of KV cache is exactly the budget. Stay at Q4_K_M — a 14B at Q5 (~11GB) leaves no room for context here.
→Keep num_ctx ≤8K with a 14B loaded; switch to the 8B when a task genuinely needs a long context on 12GB.
→A used RTX 3060 12GB is the cheapest ticket into this tier — the RTX 4070 runs the same models, just ~30–40% faster.

Quick start

Install Ollama

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

Run the winner

ollama run phi4

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Frequently asked questions

What is the best Ollama model for an RTX 3060 12GB?

Phi-4 14B (~9GB at Q4, ~25 tok/s on the 3060) for reasoning-heavy work, Qwen 3 14B as the all-rounder, and DeepSeek-R1 14B when you want visible chain-of-thought. All three are the same ~9GB size — pick by task, not by fit.

Can 12GB VRAM run a 14B model?

Yes — comfortably, and that is the whole point of this tier. A 14B at Q4_K_M uses ~9GB, leaving 2–3GB for the KV cache. It is the biggest dense class that fits; 20B+ models (Codestral, gpt-oss:20b) need 16GB.

RTX 4070 vs RTX 3060 12GB for Ollama?

Both have 12GB, so the model list is identical — the 4070 is roughly 30–40% faster tok/s. If budget decides, the used 3060 12GB runs everything on this page; the 4070 just makes the 14Bs feel snappier.

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