Yes — with 80.9 GB to spare

Mistral Small 3.2 24B at Q4_K_M fits your M3 Max · 128 GB entirely in unified memory at 8K context, at an estimated 17 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 80.9 GB of 96.0 GB

Quantisation ladder

Quant	Weights	Total @ 8K	Max context	Tok/s	Quality	Fit
Q8_0	23.4 GB	25.2 GB	128K	9.6	−0.1% ppl	Long context
Q6_K	18.0 GB	19.9 GB	128K	12	−0.4% ppl	Long context
Q5_K_M	15.6 GB	17.4 GB	128K	14	−0.8% ppl	Long context
Q4_K_M	13.3 GB	15.1 GB	128K	17	−1.9% ppl	Recommended
Q3_K_M	10.7 GB	12.6 GB	128K	21	−5.4% ppl	Long context
Q2_K	9.2 GB	11.1 GB	128K	24	−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 96.0 GB of 128 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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