Can the MacBook Pro 16" M4 Max run Qwen3-30B-A3B-Instruct-2507?

Yes. Qwen3-30B-A3B-Instruct-2507 fits the MacBook Pro 16" M4 Max's 48 GB at Q4_K_M (about 19.8 GB needed), running at an estimated 118 tok/s. There's room for up to Q8_0 if you want higher quality.

Memory
48 GB
Needs at Q4
19.8 GB
Headroom
28.2 GB
Speed (Q4_K_M)
~118 tok/s

Every quantization

Qwen3-30B-A3B-Instruct-2507 (30.5B, 3B active) on 48 GB

QuantWeightsNeedsFitEst. speed
Q4_K_M18 GB19.8 GBFits easily~118 tok/s
Q5_K_M22 GB23.8 GBFits easily~96 tok/s
Q8_033 GB34.8 GBFits~64 tok/s
FP1661 GB62.8 GBToo big -

Estimates. Needs = weights + 1.8 GB for KV cache and runtime at modest context. Speed is single-stream decode scaled from the MacBook Pro 16" M4 Max's 546 GB/s bandwidth (Estimated from memory bandwidth (llama.cpp decode)). Long contexts need more memory and run slower.

Run it

Copy, paste, go

llama-server -hf unsloth/Qwen3-30B-A3B-Instruct-2507-GGUF -ngl 99

Ollama pulls a Q4 build by default. Pick a larger quant only if the table above says it fits. Full Qwen3-30B-A3B-Instruct-2507 details →

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Same model, other hardware - and other models on this card

Qwen3-30B-A3B-Instruct-2507 on other hardware

Also runs on the MacBook Pro 16" M4 Max

Frequently asked

Quick answers

Can the Apple MacBook Pro 16" M4 Max run Qwen3-30B-A3B-Instruct-2507?

Yes. Qwen3-30B-A3B-Instruct-2507 fits the MacBook Pro 16" M4 Max's 48 GB at Q4_K_M (about 19.8 GB needed), running at an estimated 118 tok/s. There's room for up to Q8_0 if you want higher quality.

How much VRAM does Qwen3-30B-A3B-Instruct-2507 need?

About 18 GB of weights at Q4_K_M, 33 GB at Q8_0 and 61 GB at FP16. Add roughly 1.8 GB for the KV cache and runtime at modest context lengths.

How fast is Qwen3-30B-A3B-Instruct-2507 on the MacBook Pro 16" M4 Max?

Roughly 118 tokens per second at Q4_K_M (single user, llama.cpp-style decode). This is an estimate from the MacBook Pro 16" M4 Max's 546 GB/s memory bandwidth; as a mixture-of-experts model only about 3B parameters are read per token, which is why it is faster than its size suggests.