Mistral Small 3.1 — Local AI Model by Mistral AI

作者： Jakub Rusinowski · 最后更新： 2025年3月17日

Mistral最新的小型视觉模型，Apache 2.0许可证。Mistral Small 3.1 24B具备128k上下文窗口（是前代的4倍）、多模态图像理解能力，在大多数基准测试中超越Gemma 3 27B，且完全商业可用。

Licence

LicenceWhat it permitsApplies to
Apache-2.0Commercial use permitted
Commercial use permitted. No usage restrictions beyond attribution.		Mistral Small 3.1 24B

Hardware Requirements

Mistral Small 3.1 24BMin 15 GB VRAM · Q4_K_M · 128,000 ctx · ollama run mistral-small3.1

Recommended GPU

The cheapest GPU that runs Mistral Small 3.1 locally (min 15 GB VRAM) is the AMD Radeon RX 9060 XT 16GB (16 GB).

联盟营销声明: 本页部分链接为联盟推广链接——如果你通过它们购买，LLM Configurator 可能会获得佣金，而你无需支付任何额外费用。作为亚马逊联盟成员（Amazon Associate），LLM Configurator 会从符合条件的购买中获得收益。
AMD Radeon RX 9060 XT 16GB
16 GB VRAM · 160 W board power
2026年价格波动较大——请以当前商品页价格为准。
在亚马逊查看价格

How to Run Locally

Install Ollama then run: ollama run mistral-small3.1

Minimum VRAM: 15 GB. For best results use Q4_K_M quantization.

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.

Can I Run Mistral Small 3.1 on My GPU?