Can I Run Mistral Small 3.1 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?

Superseded model. Mistral Small 3.1 has been superseded by Mistral Small 3.2. This page is kept for reference; the newer family is a better starting point. View Mistral Small 3.2 →

Written by Jakub Rusinowski · Last updated March 17, 2025

Yes

Yes — Mistral Small 3.1 24B at Q3_K_M needs about 12.2 GB of the 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM) (~3.8 GB spare), at ~36.9 tok/s (estimated), with room for about 16,384 tokens of context.

Confidence: high · Recommended quantization: Q3_K_M · Estimated speed: ~36.9 tok/s

See what else this hardware can run →

Buy this hardware AMD Radeon RX 9060 XT 16GB — 16 GB VRAM · 160 W board power Deploy in the cloud now RTX 4090 on RunPod — from $0.34/hr · rate checked 2026-07

or compare on Vast.ai from $0.35/hr (typical low · varies)

As an Amazon Associate we earn from qualifying purchases. Cloud GPU links are referral links — we may earn a commission at no extra cost to you.

RTX 4090 Laptop (16 GB VRAM, 32 GB RAM) — what it gives a model

Usable memory for models	16 GB
Memory bandwidth	576 GB/s
Form factor	Laptop
Operating system	Windows or Linux
Memory upgradeable	Yes

Mistral Small 3.1 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM): memory by quantization

Quant	Memory needed	Fits 16 GB?	Max context	Est. speed	Download
F16	49.3 GB	✗ No	—	—	47.2 GB
Q8_0	27.2 GB	✗ No	—	—	25.1 GB
Q6_K	21.5 GB	✗ No	—	—	19.4 GB
Q5_K_M	18.9 GB	✗ No	—	—	16.7 GB
Q4_K_M	16.4 GB	✗ No	—	—	14.2 GB
Q3_K_M	12.2 GB	✓ Yes	16K	~36.9 tok/s	10.1 GB
Q2_K	9.9 GB	✓ Yes	32K	~46 tok/s	7.8 GB

What to watch out for

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.
1 larger variant of Mistral Small 3.1 does not fit and would need CPU offload or different hardware.

RTX 4090 laptop limitations

A mobile RTX 4090 carries 16 GB, not the desktop card's 24 GB, and is closer to a desktop 4080 in throughput — which is why it is modelled against that chip here.
Sustained throughput depends on the chassis power limit; thin laptops throttle well below the quoted figures.

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.
16 GB of VRAM on the NVIDIA GeForce RTX 4090 Laptop GPU at 576 GB/s.
32 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.

FAQ