Can I Run Llama 3.2 Vision on MacBook Pro 14" (M4 Pro, 24 GB)?

Superseded model. Llama 3.2 Vision has been superseded by Llama 4. This page is kept for reference; the newer family is a better starting point. View Llama 4 →

Written by Jakub Rusinowski · Last updated September 25, 2024

Yes — comfortably

Yes, comfortably — Llama 3.2 Vision 11B at Q8_0 needs about 13.4 GB of the 18 GB usable on MacBook Pro 14" (M4 Pro, 24 GB), leaving ~4.6 GB spare and running at ~12.1 tok/s (estimated), with room for about 32,768 tokens of context.

Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~12.1 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)

Llama 3.2 Vision on MacBook Pro 14" (M4 Pro, 24 GB): memory by quantization

Quant	Memory needed	Fits 18 GB?	Max context	Est. speed	Download
F16	23.3 GB	✗ No	—	—	21.2 GB
Q8_0	13.4 GB	✓ Yes	32K	~12.1 tok/s	11.3 GB
Q6_K	10.8 GB	✓ Yes	32K	~15.2 tok/s	8.7 GB
Q5_K_M	9.7 GB	✓ Yes	32K	~17.3 tok/s	7.5 GB
Q4_K_M	8.5 GB	✓ Yes	64K	~19.9 tok/s	6.4 GB
Q3_K_M	6.7 GB	✓ Yes	64K	~26.5 tok/s	4.5 GB
Q2_K	5.6 GB	✓ Yes	64K	~32.5 tok/s	3.5 GB

Which Llama 3.2 Vision sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Llama 3.2 Vision 90B	57.8 GB	✗ Too large	—
Llama 3.2 Vision 11B	8.5 GB	✓ Fits	~19.9 tok/s

What to watch out for

1 larger variant of Llama 3.2 Vision does not fit and would need CPU offload or different hardware.
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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