Can I Run Aya Expanse on MacBook Air (M4, 16 GB)?

作者： Jakub Rusinowski · 最后更新： 2024年10月8日

Yes

Yes — Aya Expanse 8B at Q8_0 needs about 10.4 GB of the 12 GB usable on MacBook Air (M4, 16 GB) (~1.6 GB spare), at ~7.1 tok/s (estimated), with room for about 8,192 tokens of context.

Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~7.1 tok/s

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MacBook Air (M4, 16 GB) — what it gives a model

Usable memory for models	12 GB
Memory bandwidth	120 GB/s
Form factor	Laptop
Operating system	macOS
Memory upgradeable	No — soldered

Aya Expanse on MacBook Air (M4, 16 GB): memory by quantization

Quant	Memory needed	Fits 12 GB?	Max context	Est. speed	Download
F16	17.9 GB	✗ No	—	—	16.1 GB
Q8_0	10.4 GB	✓ Yes	8K	~7.1 tok/s	8.5 GB
Q6_K	8.5 GB	✓ Yes	8K	~9 tok/s	6.6 GB
Q5_K_M	7.6 GB	✓ Yes	8K	~10.2 tok/s	5.7 GB
Q4_K_M	6.7 GB	✓ Yes	8K	~11.8 tok/s	4.8 GB
Q3_K_M	5.3 GB	✓ Yes	8K	~15.8 tok/s	3.4 GB
Q2_K	4.5 GB	✓ Yes	8K	~19.5 tok/s	2.6 GB

Which Aya Expanse sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Aya Expanse 32B	21.6 GB	✗ Too large	—
Aya Expanse 8B	6.7 GB	✓ Fits	~11.8 tok/s

What to watch out for

1 larger variant of Aya Expanse 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 Air M4 16 GB limitations

Fanless: sustained generation throttles on long runs in a way the same chip in a MacBook Pro does not.
16 GB unified memory is shared with the OS and every open app.

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.
16 GB unified memory at 120 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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