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

Superseded model. Codestral has been superseded by Devstral. This page is kept for reference; the newer family is a better starting point. View Devstral →

Written by Jakub Rusinowski · Last updated May 29, 2024

Yes

Yes — Codestral 22B at Q4_K_M needs about 16.1 GB of the 18 GB usable on MacBook Pro 14" (M4 Pro, 24 GB) (~1.9 GB spare), at ~10.1 tok/s (estimated), with room for about 16,384 tokens of context.

Confidence: high · Recommended quantization: Q4_K_M · Estimated speed: ~10.1 tok/s

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)

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

Quant	Memory needed	Fits 18 GB?	Max context	Est. speed	Download
F16	47.1 GB	✗ No	—	—	44.4 GB
Q8_0	26.3 GB	✗ No	—	—	23.6 GB
Q6_K	20.9 GB	✗ No	—	—	18.2 GB
Q5_K_M	18.4 GB	✗ No	—	—	15.7 GB
Q4_K_M	16.1 GB	✓ Yes	16K	~10.1 tok/s	13.4 GB
Q3_K_M	12.1 GB	✓ Yes	32K	~13.8 tok/s	9.5 GB
Q2_K	10 GB	✓ Yes	32K	~17.2 tok/s	7.3 GB

What to watch out for

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 computed from this model's published attention configuration.

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