Mistral Small 4 — Local AI Model by Mistral AI

Written by Jakub Rusinowski · Last updated June 15, 2026

Mistral AI's March 16, 2026 release unifying its former Magistral/Pixtral/Devstral lines into a single 119B-total / ~6.5B-active MoE model (128 experts, 4 active per token) with text + image input, reasoning, and agentic coding. Apache 2.0 licensed, 256K context. At Q4 it fits a single 24GB GPU (RTX 4090).

Hardware Requirements

Mistral Small 4 119B-A6.5B Min 24 GB VRAM · Q4_K_M · 256,000 ctx · ollama run mistral-small (community GGUF quants; check tag for 119B build)

How to Run Locally

Install Ollama then run: ollama run mistral-small (community GGUF quants; check tag for 119B build)

Minimum VRAM: 24 GB. For best results use Q4_K_M quantization.

Mistral Small 4 — Frequently Asked Questions

How much VRAM does Mistral Small 4 need?

Mistral Small 4 needs about 24 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: Mistral Small 4 119B-A6.5B (24 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.

Can I run Mistral Small 4 on an RTX 4090 (24 GB)?

Yes — Mistral Small 4 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 4?

Q4_K_M is the best balance of quality and VRAM for Mistral Small 4 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 4 with Ollama?

Install Ollama, then run: ollama run mistral-small (community GGUF quants; check tag for 119B build). This downloads Mistral Small 4 and starts a local, OpenAI-compatible endpoint — no internet connection is needed after the initial download.