Question 1

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

Accepted Answer

Yes — Apple M1 Pro (10-core GPU) runs Mistral Small 24B fully on GPU at the Q8_0 quantization. Full GPU inference — 32 GB VRAM meets the 32 GB recommendation.

Question 2

What quantization of Mistral Small 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 Q8_0 variant is the best fit. Estimated ~7.6 tokens/sec on the Q8_0 quantization.

Question 3

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

Accepted Answer

Roughly 7.6 tokens/sec for Q8_0. 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 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	14 GB	16 GB	20 GB	8K / 32K	Full GPU	~11.4
Q8_0Best fit	25 GB	27 GB	32 GB	8K / 32K	Full GPU	~7.6

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

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