Can I Run Yi 1.5 Family on MacBook Pro 14" (M4 Pro, 24 GB)?

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Written by Jakub Rusinowski · Last updated May 13, 2024

Yes — comfortably

Yes, comfortably — Yi 1.5 9B Chat at Q8_0 needs about 11 GB of the 18 GB usable on MacBook Pro 14" (M4 Pro, 24 GB), leaving ~7 GB spare and running at ~14.6 tok/s (estimated), with room for about 16,384 tokens of context.

Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~14.6 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)

Yi 1.5 Family on MacBook Pro 14" (M4 Pro, 24 GB): memory by quantization

Quant	Memory needed	Fits 18 GB?	Max context	Est. speed	Download
F16	19.3 GB	✗ No	—	—	17.7 GB
Q8_0	11 GB	✓ Yes	16K	~14.6 tok/s	9.4 GB
Q6_K	8.8 GB	✓ Yes	16K	~18.5 tok/s	7.2 GB
Q5_K_M	7.9 GB	✓ Yes	16K	~21 tok/s	6.3 GB
Q4_K_M	6.9 GB	✓ Yes	16K	~24.2 tok/s	5.3 GB
Q3_K_M	5.4 GB	✓ Yes	16K	~32.4 tok/s	3.8 GB
Q2_K	4.5 GB	✓ Yes	16K	~39.9 tok/s	2.9 GB

Which Yi 1.5 Family sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Yi 1.5 34B Chat	23.6 GB	✗ Too large	—
Yi 1.5 9B Chat	6.9 GB	✓ Fits	~24.2 tok/s

What to watch out for

1 larger variant of Yi 1.5 Family 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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