Can I Run Mistral Small 3.2 on MacBook Pro 16" (M5 Max, 128 GB)?

Written by Jakub Rusinowski · Last updated September 6, 2026

MacBook Pro 16" (M5 Max, 128 GB) — what it gives a model

Usable memory for models	96 GB
Memory bandwidth	614 GB/s
Form factor	Laptop
Operating system	macOS
Memory upgradeable	No — soldered

Mistral Small 3.2 on MacBook Pro 16" (M5 Max, 128 GB): memory by quantization

Quant	Memory needed	Fits 96 GB?	Max context	Est. speed	Download
F16	49.6 GB	✓ Yes	128K	~6.9 tok/s	47.2 GB
Q8_0	27.5 GB	✓ Yes	128K	~12.5 tok/s	25.1 GB
Q6_K	21.8 GB	✓ Yes	128K	~15.9 tok/s	19.4 GB
Q5_K_M	19.2 GB	✓ Yes	128K	~18.1 tok/s	16.7 GB
Q4_K_M	16.7 GB	✓ Yes	128K	~20.9 tok/s	14.2 GB
Q3_K_M	12.5 GB	✓ Yes	128K	~28.3 tok/s	10.1 GB
Q2_K	10.2 GB	✓ Yes	128K	~35.1 tok/s	7.8 GB

What to watch out for

• This model does not publish its full attention configuration, so the KV-cache share of these figures is inferred from its parameter count rather than computed exactly.
• Memory on this machine is not upgradeable, so the configuration you buy is the ceiling for every model you will ever run on it.

MacBook Pro M5 Max 128 GB limitations

• Unified memory is soldered and cannot be upgraded after purchase.

Recommended setup

Ollama or LM Studio (Metal) — MLX for the fastest Apple-native throughput

How these numbers are calculated

• Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
• 128 GB unified memory at 614 GB/s, shared between CPU and GPU.
• macOS reserves a share of unified memory for the system, so not all of it is available to a model.
• Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
• KV cache is inferred from the parameter count because this model does not publish its full attention configuration.

FAQ

Can I run Mistral Small 3.2 on MacBook Pro 16" (M5 Max, 128 GB)?

Yes, comfortably — Mistral Small 3.2 24B at Q8_0 needs about 27.5 GB of the 96 GB usable on MacBook Pro 16" (M5 Max, 128 GB), leaving ~68.5 GB spare and running at ~12.5 tok/s (estimated), with room for about 131,072 tokens of context.

Which quantization of Mistral Small 3.2 should I use on MacBook Pro 16" (M5 Max, 128 GB)?

Q8_0 — it needs about 27.5 GB of the 96 GB available, downloads as roughly 25.1 GB, and runs at an estimated 12.5 tokens/sec with up to 128K of context.

What limits Mistral Small 3.2 on MacBook Pro 16" (M5 Max, 128 GB)?

Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.

Which runtime should I use?

Ollama or LM Studio (Metal) — MLX for the fastest Apple-native throughput

Other Computers

• Mistral Small 3.2 on MacBook Pro M4 Max 128 GB
• Mistral Small 3.2 on MacBook Pro M4 Max 48 GB
• Mistral Small 3.2 on MacBook Pro M4 Pro 24 GB
• Mistral Small 3.2 on MacBook Air M4 16 GB

Other Models on MacBook Pro 16" (M5 Max, 128 GB)

• Mistral Small 4 on MacBook Pro 16" (M5 Max, 128 GB)
• Nemotron 3 Nano Omni on MacBook Pro 16" (M5 Max, 128 GB)
• Nemotron 3 Super on MacBook Pro 16" (M5 Max, 128 GB)
• Nemotron 70B on MacBook Pro 16" (M5 Max, 128 GB)
• Nemotron Cascade 2 on MacBook Pro 16" (M5 Max, 128 GB)

Mistral Small 3.2 on GPUs

• Mistral Small 3.2 on NVIDIA GeForce RTX 5090
• Mistral Small 3.2 on NVIDIA GeForce RTX 5080
• Mistral Small 3.2 on NVIDIA GeForce RTX 5070 Ti
• Mistral Small 3.2 on NVIDIA GeForce RTX 5070

What This Model Is Good At

• Best local LLMs for coding
• Best local LLMs for general assistant
• Best local LLMs for agents

Model & Tools

• Mistral Small 3.2 model page
• VRAM calculator
• Check your own hardware

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