Can I Run Qwen 2.5 Family on MacBook Pro 14" (M4 Pro, 24 GB)?

Superseded model. Qwen 2.5 Family has been superseded by Qwen 3. This page is kept for reference; the newer family is a better starting point. View Qwen 3 →

Written by Jakub Rusinowski · Last updated November 12, 2024

Yes, but it is tight

Yes, but it is tight — Qwen 2.5 14B Instruct at Q8_0 needs about 17.3 GB of the 18 GB usable on MacBook Pro 14" (M4 Pro, 24 GB), leaving only ~0.7 GB before the runtime starts swapping. Expect ~9.3 tok/s (estimated), with room for about 8,192 tokens of context.

Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~9.3 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)

Qwen 2.5 Family on MacBook Pro 14" (M4 Pro, 24 GB): memory by quantization

Quant	Memory needed	Fits 18 GB?	Max context	Est. speed	Download
F16	30.4 GB	✗ No	—	—	28 GB
Q8_0	17.3 GB	✓ Yes	8K	~9.3 tok/s	14.9 GB
Q6_K	13.9 GB	✓ Yes	16K	~11.8 tok/s	11.5 GB
Q5_K_M	12.3 GB	✓ Yes	32K	~13.4 tok/s	9.9 GB
Q4_K_M	10.9 GB	✓ Yes	32K	~15.4 tok/s	8.5 GB
Q3_K_M	8.4 GB	✓ Yes	32K	~20.7 tok/s	6 GB
Q2_K	7 GB	✓ Yes	32K	~25.5 tok/s	4.6 GB

Which Qwen 2.5 Family sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Qwen 2.5 72B Instruct	47 GB	✗ Too large	—
Qwen 2.5 Coder 32B	22.3 GB	✗ Too large	—
Qwen 2.5 14B Instruct	10.9 GB	✓ Fits	~15.4 tok/s
Qwen 2.5 7B Instruct	5.9 GB	✓ Fits	~28.4 tok/s

What to watch out for

Only ~0.7 GB of headroom at Q8_0: a longer context or a second application can push this into swapping.
2 larger variants of Qwen 2.5 Family do 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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