Mistral Small 3.1 — local AI model by Mistral AI

Written by Jakub Rusinowski · Last updated

Superseded model. Mistral Small 3.1 has been superseded by Mistral Small 3.2.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 →

Mistral's latest small model, now with vision capabilities and Apache 2.0 license. Mistral Small 3.1 24B features a 128k context window (4× larger than its predecessor), multimodal image understanding, and relicensed to Apache 2.0 for full commercial use — while outperforming Gemma 3 27B on most benchmarks.

Mistral AI Hugging Face

Variants

The smallest Mistral Small 3.1 variant needs about 15 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache.

ModelVRAM
Mistral Small 3.1 24B →
24B
~15 GB

Memory is quantized weights plus overhead at Q4_K_M, from the same engine as the GPU & VRAM checker.

How to run Mistral Small 3.1 locally

Install Ollama, then pull the tag.

ollama run mistral-small3.1

Pick a size above for its own VRAM figure, speed estimate and install command.

Licence

Apache-2.0Commercial use permitted

Commercial use permitted. No usage restrictions beyond attribution.

Applies to: Mistral Small 3.1 24B

Recommended GPU

The cheapest catalogued GPU that runs Mistral Small 3.1 locally (min 15 GB VRAM) is the AMD Radeon RX 7900 XT (20 GB).

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AMD Radeon RX 7900 XT 20GB
20 GB VRAM · 315 W board power
2026 prices are volatile — check the current listing.

Can I run Mistral Small 3.1 on my GPU?

Mistral Small 3.1 — frequently asked questions

How much VRAM does Mistral Small 3.1 need?

Mistral Small 3.1 needs about 15 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: Mistral Small 3.1 24B (15 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.

Can I run Mistral Small 3.1 on an RTX 4090 (24 GB)?

Yes — Mistral Small 3.1 runs on an RTX 4090 (24 GB) and other 24 GB cards such as the RTX 3090. Smaller variants also fit comfortably on 8–16 GB GPUs at Q4_K_M.

What quantization should I use for Mistral Small 3.1?

Q4_K_M is the best balance of quality and VRAM for Mistral Small 3.1 in most cases. Choose Q8_0 for near-lossless quality if you have spare VRAM, or smaller quants (Q3/Q2) only when memory is tight.

How do I run Mistral Small 3.1 with Ollama?

Install Ollama, then run: ollama run mistral-small3.1. This downloads Mistral Small 3.1 and starts a local, OpenAI-compatible endpoint — no internet connection is needed after the initial download.