Yes — with 20.9 GB to spare

Mistral Small 3.2 24B at Q4_K_M fits your M4 Pro · 48 GB entirely in unified memory at 8K context, at an estimated 11 tokens per second. There is room for its full 128K window.

Fully in unified memory 8K context Q4_K_M · 13.3 GB Apache 2.0 Released Jun 2025 Vision

Apache-2.0, vision-capable, and the most 24 GB-friendly of the 2025 generalists.

What hardware do I need for Mistral Small 3.2 24B? →

The VRAM budget

weights 13.3 GB

Weights 13.3 GB KV cache @ 8K 1.25 GB Runtime overhead 0.6 GB Free 20.9 GB of 36.0 GB

Quantisation ladder

Quant	Weights	Total @ 8K	Max context	Tok/s	Quality	Fit
Q8_0	23.4 GB	25.2 GB	77K	6.5	−0.1% ppl	Long context
Q6_K	18.0 GB	19.9 GB	111K	8.5	−0.4% ppl	Long context
Q5_K_M	15.6 GB	17.4 GB	126K	9.8	−0.8% ppl	Long context
Q4_K_M	13.3 GB	15.1 GB	128K	11	−1.9% ppl	Recommended
Q3_K_M	10.7 GB	12.6 GB	128K	14	−5.4% ppl	Long context
Q2_K	9.2 GB	11.1 GB	128K	17	−15% ppl	Long context

Quality is the published perplexity delta against f16 weights. Max context assumes an f16 KV cache; q8_0 roughly doubles it.

How to run it

Ollama llama.cpp LM Studio MLX

terminal

$ pip install mlx-lm
$ mlx_lm.generate --model mlx-community/Mistral-Small-3.2-24B-Instruct-2506-4bit \
    --max-tokens 512 --prompt "Hello"

Apple's own array framework. The fastest path on Apple Silicon. More on MLX.

01Download is 13.3 GB. Keep it on an SSD — a first load off a spinning disk takes minutes.

02macOS caps what the GPU may wire down at about 36.0 GB of 48 GB. Raising it with iogpu.wired_limit_mb is possible, and risky.

03There is room to go to the model's full 128K context on this card.

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