Can I Run Magistral Small on MacBook Pro 14" (M4 Pro, 24 GB)?

Written by Jakub Rusinowski · Last updated September 6, 2026

Yes, but it is tight

Yes, but it is tight — Magistral Small 24B at Q4_K_M needs about 17 GB of the 18 GB usable on MacBook Pro 14" (M4 Pro, 24 GB), leaving only ~1 GB before the runtime starts swapping. Expect ~9.5 tok/s (estimated), with room for about 8,192 tokens of context.

Confidence: low · Recommended quantization: Q4_K_M · Estimated speed: ~9.5 tok/s

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MacBook Pro 14" (M4 Pro, 24 GB) — what it gives a model

Usable memory for models	18 GB
Memory bandwidth	273 GB/s
Form factor	Laptop
Operating system	macOS
Memory upgradeable	No — soldered
Price	$1,999 (lib/data/laptops.ts (street price), checked 2026-07-06)

Magistral Small on MacBook Pro 14" (M4 Pro, 24 GB): memory by quantization

Quant	Memory needed	Fits 18 GB?	Max context	Est. speed	Download
F16	50.5 GB	✗ No	—	—	48 GB
Q8_0	28 GB	✗ No	—	—	25.5 GB
Q6_K	22.2 GB	✗ No	—	—	19.7 GB
Q5_K_M	19.5 GB	✗ No	—	—	17 GB
Q4_K_M	17 GB	✓ Yes	8K	~9.5 tok/s	14.5 GB
Q3_K_M	12.7 GB	✓ Yes	32K	~13 tok/s	10.2 GB
Q2_K	10.4 GB	✓ Yes	32K	~16.3 tok/s	7.9 GB

What to watch out for

Only ~1 GB of headroom at Q4_K_M: a longer context or a second application can push this into swapping.
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 M4 Pro 24 GB limitations

Unified memory is soldered and cannot be upgraded after purchase.
24 GB is the practical ceiling for a single mid-size model plus a long context.

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.
24 GB unified memory at 273 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.

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