Best Quantization for 6 GB VRAM: Which Level Fits?

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Quick Answer

Q4_K_M is the sweet spot — 7B/8B models at Q4_K_M use 4.7–4.9 GB, leaving 1.1 GB for the KV-cache. Q5_K_M fits but requires limiting context to 2k tokens. Avoid Q6_K and above on 6 GB cards.

▸Llama 3.3 8B / Mistral Small / Qwen 3 7B at Q4_K_M: 4.7–4.9 GB — safe 6 GB fit with 4k context
▸Q5_K_M uses ~5.7 GB — fits but cap context to 2k tokens or risk OOM mid-session
▸14B models at Q4_K_M need 9.3 GB — no viable quantization fits 6 GB at acceptable quality

Updated: 2026-05

Quantization & VRAMBeginner

Key Takeaways

✓For 6 GB VRAM cards (RTX 3060 6 GB, RTX 3050 6 GB, GTX 1660 Ti 6 GB): Q4_K_M is the correct quantization for 7B and 8B models
✓Q4_K_M leaves 1.1 GB free — enough for a 4k-token KV-cache at the default Ollama context size of 2048
✓Q5_K_M improves perplexity by ~1 point but uses 5.7 GB; reduce `--ctx-size` to 2048 to avoid out-of-memory errors
✓14B models (Qwen 3 14B, Llama 3.3 13B) require 9.3 GB at Q4_K_M — no quantization tier makes them viable on 6 GB

Quantization VRAM Usage for 7B/8B Models on 6 GB

Quantization level directly controls how much VRAM a model occupies. For 7B and 8B parameter models — the largest class that fits a 6 GB GPU — the practical options are Q3_K_M through Q5_K_M. Q2_K fits but degrades quality below useful levels; Q6_K and above exceed the 6 GB ceiling.

Q4_K_M is the recommended default: a 7B model uses approximately 4.7 GB and an 8B model uses 4.9 GB at this quantization. This leaves 1.1 GB for the KV-cache, which Ollama allocates for the context window. At the default 2048-token context, this is sufficient. Increasing context to 4096 tokens requires approximately 0.5 GB additional KV-cache on a 7B model — still within budget on most 6 GB cards.

Q5_K_M is the next step up. An 8B model at Q5_K_M uses approximately 5.7 GB, leaving only 300 MB free. This is enough for very short contexts (512–2048 tokens) but will cause OOM errors with longer conversations or system prompts. Use Q5_K_M only if you keep `num_ctx` at 2048 or below.

Quantization	7B VRAM	8B VRAM	Fits 6 GB?	Max Context (approx)
Q2_K	~2.8 GB	~3.0 GB	✓ (quality poor)	8k+
Q3_K_M	~3.5 GB	~3.7 GB	✓ (acceptable)	8k+
Q4_K_M	~4.7 GB	~4.9 GB	✓ recommended	4k
Q5_K_M	~5.5 GB	~5.7 GB	⚠ tight (2k ctx only)	2k
Q6_K	~6.4 GB	~6.6 GB	✗ OOM	—
Q8_0	~7.5 GB	~7.7 GB	✗ OOM	—

Best Models to Run at Q4_K_M on 6 GB VRAM

Three 7B/8B models stand out at Q4_K_M on a 6 GB card. Qwen 3 7B Instruct is the best all-rounder — strong coding (HumanEval ~60%), multilingual support, and 128k context architecture (though you will run at 4k due to VRAM). Run it with `ollama run qwen2.5:7b`.

Llama 3.3 8B is the fastest option. At Q4_K_M it runs at approximately 25 tokens per second on an RTX 3060 6 GB and handles general chat and instruction-following reliably. MMLU score of 66.6% is lower than Qwen 3 7B but the speed advantage makes it the better pick for interactive sessions.

Phi-4 Mini (3.8B) is the wild card. At Q8_0 it fits in approximately 4.1 GB — comfortably within 6 GB — and punches above its weight on reasoning benchmarks relative to its size. Use it when you need sub-5 GB footprint with better reasoning than older 7B models. Run with `ollama run phi4-mini`.

Do not attempt 14B models on 6 GB. Qwen 3 14B at Q4_K_M requires 9.3 GB. Q2_K brings it to approximately 5.5 GB but the perplexity penalty is severe — the model produces noticeably degraded output. Stick to 7B/8B at Q4_K_M or 3B/4B at Q8_0.

Related Guides

▸Best Local LLM for 6 GB VRAM -- 6GB VRAM guide
▸Best Ollama Models for 4 GB VRAM -- 4GB VRAM models
▸Q4_K_M vs Q8_0: Which Quantization to Use? -- quantization comparison
▸What Is Q4_K_M Quantization? -- quantization explainer

Quick Answers About Quantization on 6 GB VRAM

Can I run a 14B model on 6 GB VRAM?▾

No viable path exists. Qwen 3 14B at Q4_K_M needs 9.3 GB. Dropping to Q2_K brings it to approximately 5.5 GB but the quality degradation is severe — output becomes noticeably less coherent. The correct model for 6 GB VRAM is a 7B or 8B model at Q4_K_M.

Is Q4_K_M or Q4_K_S better for 6 GB VRAM?▾

Q4_K_M. The Q4_K_S variant saves about 200 MB versus Q4_K_M but with a larger perplexity penalty. On a 6 GB card, Q4_K_M already leaves 1.1 GB headroom — the extra 200 MB from Q4_K_S is not needed, and the quality tradeoff is not worth it.

Should I use Q5_K_M instead of Q4_K_M at 6 GB VRAM?▾

Only if you strictly limit context to 2k tokens. Q5_K_M improves perplexity by approximately 1–1.5 points over Q4_K_M, but uses 5.7 GB on an 8B model, leaving only 300 MB for the KV-cache. Set `num_ctx 2048` in your Modelfile or Ollama parameters to avoid OOM mid-session.

What happens if my model exceeds 6 GB VRAM?▾

Ollama offloads the overflow layers to CPU RAM (using llama.cpp layer offloading). This causes a dramatic speed drop — from ~25 tok/s GPU-only to ~3–5 tok/s with partial CPU offload. If you see "n_gpu_layers" warnings or tokens-per-second below 5, your model is too large for your VRAM at the selected quantization.

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