Can I Run Qwen3-Coder on MacBook Pro 16" (M5 Max, 128 GB)?

Written by Jakub Rusinowski · Last updated September 29, 2026

Yes, but it is tight

Yes, but it is tight — Qwen3-Coder-Next (80B-A3B MoE) at Q8_0 needs about 88.3 GB of the 96 GB usable on MacBook Pro 16" (M5 Max, 128 GB), leaving only ~7.7 GB before the runtime starts swapping. Expect ~61.9 tok/s (estimated), with room for about 32,768 tokens of context.

Confidence: low · Recommended quantization: Q8_0 · Estimated speed: ~61.9 tok/s

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MacBook Pro 16" (M5 Max, 128 GB) — what it gives a model

Usable memory for models	96 GB
Memory bandwidth	614 GB/s
Form factor	Laptop
Operating system	macOS
Memory upgradeable	No — soldered

Qwen3-Coder on MacBook Pro 16" (M5 Max, 128 GB): memory by quantization

Quant	Memory needed	Fits 96 GB?	Max context	Est. speed	Download
F16	163.3 GB	✗ No	—	—	160 GB
Q8_0	88.3 GB	✓ Yes	32K	~61.9 tok/s	85 GB
Q6_K	68.9 GB	✓ Yes	64K	~71.4 tok/s	65.6 GB
Q5_K_M	60 GB	✓ Yes	64K	~76.9 tok/s	56.7 GB
Q4_K_M	51.6 GB	✓ Yes	128K	~82.8 tok/s	48.3 GB
Q3_K_M	37.4 GB	✓ Yes	128K	~95.3 tok/s	34.1 GB
Q2_K	29.6 GB	✓ Yes	128K	~104 tok/s	26.3 GB

Which Qwen3-Coder sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Qwen3-Coder 480B-A35B (MoE)	291.6 GB	✗ Too large	—
Qwen3-Coder-Next (80B-A3B MoE)	51.6 GB	✓ Fits	~82.8 tok/s
Qwen3-Coder 30B-A3B (MoE)	20 GB	✓ Fits	~98.8 tok/s
Qwen3-Coder 8B	6.8 GB	✓ Fits	~52.5 tok/s

What to watch out for

1 larger variant of Qwen3-Coder 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.

MacBook Pro M5 Max 128 GB limitations

Unified memory is soldered and cannot be upgraded after purchase.

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.
128 GB unified memory at 614 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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