Question 1

Can I run Mistral Small 3.1 24B on a Apple M2 Ultra?

Accepted Answer

Yes — Apple M2 Ultra runs Mistral Small 3.1 24B fully on GPU at the FP16 quantization. Full GPU inference — 192 GB VRAM meets the 54 GB recommendation.

Question 2

What quantization of Mistral Small 3.1 24B should I use on a Apple M2 Ultra?

Accepted Answer

For 192 GB VRAM on the Apple M2 Ultra, the FP16 variant is the best fit. Estimated ~17.5 tokens/sec on the FP16 quantization.

Question 3

How fast does Mistral Small 3.1 24B run on Apple M2 Ultra?

Accepted Answer

Roughly 17.5 tokens/sec for FP16. Real speed depends on context length, backend (Ollama, llama.cpp, LM Studio), and KV cache size.

Question 4

What if Apple M2 Ultra is not enough for Mistral Small 3.1 24B?

Accepted Answer

Try a smaller quantization variant or run on CPU + RAM with the trade-off of slower generation.

Quantization	File Size	Min VRAM	Rec VRAM	Context	Verdict	Estimated tok/s
Q4_K_M	12.6 GB	14 GB	16 GB	8K / 128K	Full GPU	~50.8
FP16Best fit	45.6 GB	48 GB	54 GB	8K / 128K	Full GPU	~17.5

Can I Run Mistral Small 3.1 24B on Apple M2 Ultra?

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Every Mistral Small 3.1 24B quantization on Apple M2 Ultra