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

Superseded model. Mistral Family has been superseded by Mistral Small 4.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. View Mistral Small 4 →

Written by Jakub Rusinowski · Last updated January 28, 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 ~32.7 tok/s (estimated), with room for about 8,192 tokens of context.

Confidence: medium · Recommended quantization: Q3_K_M · Estimated speed: ~32.7 tok/s

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

Usable memory for models8 GB
Memory bandwidth272 GB/s
Form factorLaptop
Operating systemWindows or Linux
Memory upgradeableYes
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

QuantMemory neededFits 8 GB?Max contextEst. speedDownload
F1626.1 GB✗ No24 GB
Q8_014.9 GB✗ No12.8 GB
Q6_K12 GB✗ No9.8 GB
Q5_K_M10.6 GB✗ No8.5 GB
Q4_K_M9.4 GB✗ No7.2 GB
Q3_K_M7.3 GB✓ Yes8K~32.7 tok/s5.1 GB
Q2_K6.1 GB✓ Yes16K~40.2 tok/s3.9 GB

Which Mistral Family sizes fit

VariantNeeds at Q4_K_MFits?Est. speed
Mistral Small 3 (24B)17.2 GB✗ Too large
Mistral NeMo 12B9.4 GB✗ Too large

What to watch out for

RTX 4060 laptop limitations

Recommended setup

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

How these numbers are calculated

FAQ

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

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 ~32.7 tok/s (estimated), with room for about 8,192 tokens of context.

Which quantization of Mistral Family should I use on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)?

Q3_K_M — it needs about 7.3 GB of the 8 GB available, downloads as roughly 5.1 GB, and runs at an estimated 32.7 tokens/sec with up to 8K of context.

What limits Mistral Family on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)?

Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.

Which runtime should I use?

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

Other Computers

Other Models on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)

Mistral Family on GPUs

What This Model Is Good At

Model & Tools

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