Question 1

Can I run Mistral Small 3.1 24B on a Apple M1 Pro (10-core GPU)?

Accepted Answer

Yes — Apple M1 Pro (10-core GPU) runs Mistral Small 3.1 24B fully on GPU at the Q4_K_M quantization. Full GPU inference — 32 GB VRAM meets the 16 GB recommendation.

Question 2

What quantization of Mistral Small 3.1 24B should I use on a Apple M1 Pro (10-core GPU)?

Accepted Answer

For 32 GB VRAM on the Apple M1 Pro (10-core GPU), the Q4_K_M variant is the best fit. Estimated ~12.7 tokens/sec on the Q4_K_M quantization.

Question 3

How fast does Mistral Small 3.1 24B run on Apple M1 Pro (10-core GPU)?

Accepted Answer

Roughly 12.7 tokens/sec for Q4_K_M. Real speed depends on context length, backend (Ollama, llama.cpp, LM Studio), and KV cache size.

Question 4

What if Apple M1 Pro (10-core GPU) 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_MBest fit	12.6 GB	14 GB	16 GB	8K / 128K	Full GPU	~12.7
FP16	45.6 GB	48 GB	54 GB	8K / 128K	Hybrid CPU+GPU	~2

Can I Run Mistral Small 3.1 24B on Apple M1 Pro (10-core GPU)?

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Every Mistral Small 3.1 24B quantization on Apple M1 Pro (10-core GPU)