Can I Run DeepSeek V4 on MacBook Pro 16" (M5 Max, 128 GB)?

Written by Jakub Rusinowski · Last updated April 24, 2026

Yes, but it is tight

Yes, but it is tight — DeepSeek V4-Flash at Q2_K needs about 94.7 GB of the 96 GB usable on MacBook Pro 16" (M5 Max, 128 GB), leaving only ~1.3 GB before the runtime starts swapping. Expect ~60.4 tok/s (estimated), with room for about 16,384 tokens of context.

Confidence: low · Recommended quantization: Q2_K · Estimated speed: ~60.4 tok/s

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

DeepSeek V4 on MacBook Pro 16" (M5 Max, 128 GB): memory by quantization

Quant	Memory needed	Fits 96 GB?	Max context	Est. speed	Download
F16	569.4 GB	✗ No	—	—	568 GB
Q8_0	303.1 GB	✗ No	—	—	301.8 GB
Q6_K	234.3 GB	✗ No	—	—	232.9 GB
Q5_K_M	202.7 GB	✗ No	—	—	201.3 GB
Q4_K_M	172.8 GB	✗ No	—	—	171.5 GB
Q3_K_M	122.4 GB	✗ No	—	—	121.1 GB
Q2_K	94.7 GB	✓ Yes	16K	~60.4 tok/s	93.4 GB

Which DeepSeek V4 sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
DeepSeek V4-Pro	967.4 GB	✗ Too large	—
DeepSeek V4-Flash	172.8 GB	✗ Too large	—

What to watch out for

Only ~1.3 GB of headroom at Q2_K: a longer context or a second application can push this into swapping.
Q2_K is the only quantization that fits, and it is a heavily degraded one — expect noticeably worse output than the same model at Q4_K_M. A smaller model at Q4 is usually the better trade.
2 larger variants of DeepSeek V4 do 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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