Written by Jakub Rusinowski · Last updated September 6, 2026
A reliability release rather than a capability one. Same 24B architecture as Small 3.1, with instruction-following, function-calling robustness and output stability fixed — Arena Hard more than doubles (19.56% to 43.10%) and the infinite-repetition rate halves. If you were running 3.1, this is a drop-in upgrade with no new hardware cost.
| Licence | What it permits | Applies to |
|---|---|---|
Apache-2.0 | Commercial use permitted Commercial use permitted. No usage restrictions beyond attribution. | Mistral Small 3.2 24B |
| Mistral Small 3.2 24B | Min 15 GB VRAM · Q4_K_M · 131,072 ctx · ollama run mistral-small:24b |
The cheapest GPU that runs Mistral Small 3.2 locally (min 15 GB VRAM) is the AMD Radeon RX 9060 XT 16GB (16 GB).
Install Ollama then run: ollama run mistral-small:24b
Minimum VRAM: 15 GB. For best results use Q4_K_M quantization.
Mistral Small 3.2 needs about 15 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: Mistral Small 3.2 24B (15 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.
Yes — Mistral Small 3.2 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.
Q4_K_M is the best balance of quality and VRAM for Mistral Small 3.2 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.
Install Ollama, then run: ollama run mistral-small:24b. This downloads Mistral Small 3.2 and starts a local, OpenAI-compatible endpoint — no internet connection is needed after the initial download.