Can I Run Qwen3.8 on Mac Studio (M4 Max, 64 GB)?

Written by Jakub Rusinowski · Last updated September 6, 2026

Yes — comfortably

Yes, comfortably — Qwen3.8 27B at Q8_0 needs about 32.1 GB of the 48 GB usable on Mac Studio (M4 Max, 64 GB), leaving ~15.9 GB spare and running at ~9.6 tok/s (estimated), with room for about 65,536 tokens of context.

Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~9.6 tok/s

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Mac Studio (M4 Max, 64 GB) — what it gives a model

Usable memory for models	48 GB
Memory bandwidth	546 GB/s
Form factor	Desktop
Operating system	macOS
Memory upgradeable	No — soldered

Qwen3.8 on Mac Studio (M4 Max, 64 GB): memory by quantization

Quant	Memory needed	Fits 48 GB?	Max context	Est. speed	Download
F16	58.1 GB	✗ No	—	—	55.6 GB
Q8_0	32.1 GB	✓ Yes	64K	~9.6 tok/s	29.5 GB
Q6_K	25.3 GB	✓ Yes	64K	~12.2 tok/s	22.8 GB
Q5_K_M	22.2 GB	✓ Yes	64K	~13.9 tok/s	19.7 GB
Q4_K_M	19.3 GB	✓ Yes	128K	~16.1 tok/s	16.8 GB
Q3_K_M	14.4 GB	✓ Yes	128K	~22 tok/s	11.8 GB
Q2_K	11.7 GB	✓ Yes	128K	~27.4 tok/s	9.1 GB

Which Qwen3.8 sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Qwen3.8-Max	1457.5 GB	✗ Too large	—
Qwen3.8 27B	19.3 GB	✓ Fits	~16.1 tok/s

What to watch out for

1 larger variant of Qwen3.8 does not fit and would need CPU offload or different hardware.
This model does not publish its full attention configuration, so the KV-cache share of these figures is inferred from its parameter count rather than computed exactly.
Memory on this machine is not upgradeable, so the configuration you buy is the ceiling for every model you will ever run on it.

Mac Studio M4 Max 64 GB limitations

Memory is soldered; the configuration chosen at purchase is permanent.

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.
64 GB unified memory at 546 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 inferred from the parameter count because this model does not publish its full attention configuration.

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