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

Superseded model. Ministral has been superseded by Mistral Small 4.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 October 16, 2024

Yes, but it is tight

Yes, but it is tight — Ministral 8B at Q5_K_M needs about 7.7 GB of the 8 GB usable on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM), leaving only ~0.3 GB before the runtime starts swapping. Expect ~30.2 tok/s (estimated), with room for about 8,192 tokens of context.

Confidence: medium · Recommended quantization: Q5_K_M · Estimated speed: ~30.2 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)

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

QuantMemory neededFits 8 GB?Max contextEst. speedDownload
F1618 GB✗ No16 GB
Q8_010.5 GB✗ No8.5 GB
Q6_K8.6 GB✗ No6.6 GB
Q5_K_M7.7 GB✓ Yes8K~30.2 tok/s5.7 GB
Q4_K_M6.9 GB✓ Yes8K~34.6 tok/s4.8 GB
Q3_K_M5.4 GB✓ Yes16K~45.5 tok/s3.4 GB
Q2_K4.6 GB✓ Yes16K~55.2 tok/s2.6 GB

Which Ministral sizes fit

VariantNeeds at Q4_K_MFits?Est. speed
Ministral 8B6.9 GB✓ Fits~34.6 tok/s
Ministral 3B3.8 GB✓ Fits~66.5 tok/s

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 Ministral on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)?

Yes, but it is tight — Ministral 8B at Q5_K_M needs about 7.7 GB of the 8 GB usable on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM), leaving only ~0.3 GB before the runtime starts swapping. Expect ~30.2 tok/s (estimated), with room for about 8,192 tokens of context.

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

Q5_K_M — it needs about 7.7 GB of the 8 GB available, downloads as roughly 5.7 GB, and runs at an estimated 30.2 tokens/sec with up to 8K of context.

What limits Ministral 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)

Ministral on GPUs

What This Model Is Good At

Model & Tools

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