Can I Run Mistral Family on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)?

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 28 stycznia 2025

Yes, but it is tight

Yes, but it is tight — Mistral NeMo 12B at Q3_K_M needs about 7.3 GB of the 8 GB usable on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM), leaving only ~0.7 GB before the runtime starts swapping. Expect ~30.9 tok/s (estimated), with room for about 8,192 tokens of context.

Confidence: medium · Recommended quantization: Q3_K_M · Estimated speed: ~30.9 tok/s

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RTX 4060 Laptop (8 GB VRAM, 16 GB RAM) — what it gives a model

Usable memory for models	8 GB
Memory bandwidth	256 GB/s
Form factor	Laptop
Operating system	Windows or Linux
Memory upgradeable	Yes
Price	$1,099 (lib/data/laptops.ts (street price), checked 2026-07-06)

Mistral Family on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM): memory by quantization

Quant	Memory needed	Fits 8 GB?	Max context	Est. speed	Download
F16	26.1 GB	✗ No	—	—	24 GB
Q8_0	14.9 GB	✗ No	—	—	12.8 GB
Q6_K	12 GB	✗ No	—	—	9.8 GB
Q5_K_M	10.6 GB	✗ No	—	—	8.5 GB
Q4_K_M	9.4 GB	✗ No	—	—	7.2 GB
Q3_K_M	7.3 GB	✓ Yes	8K	~30.9 tok/s	5.1 GB
Q2_K	6.1 GB	✓ Yes	16K	~38 tok/s	3.9 GB

Superseded model. Mistral Family has been superseded by Mistral Small 4. This page is kept for reference; the newer family is a better starting point. Mistral Small 4 needs at least 75.5 GB at Q4_K_M with an 8K context, so it does not fit the RTX 4060 Laptop (8 GB VRAM, 16 GB RAM).

Which Mistral Family sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Mistral Small 3 (24B)	17.2 GB	✗ Too large	—
Mistral NeMo 12B	9.4 GB	✗ Too large	—

What to watch out for

Only ~0.7 GB of headroom at Q3_K_M: a longer context or a second application can push this into swapping.
Q3_K_M is the only quantization that fits, and it is a heavily degraded one — expect noticeably worse output than the same model at Q4_K_M. A smaller model at Q4 is usually the better trade.
2 larger variants of Mistral Family do not fit and would need CPU offload or different hardware.

RTX 4060 laptop limitations

8 GB VRAM limits you to 7–8B models at Q4 with a short context.
System RAM is usually upgradeable on this class of laptop even though VRAM is not.

Recommended setup

Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving

How these numbers are calculated

Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
8 GB of VRAM on the NVIDIA GeForce RTX 4060 Laptop GPU at 256 GB/s.
16 GB of system RAM available for CPU offload when a model exceeds VRAM.
Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
KV cache is computed from this model's published attention configuration.

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