Can I Run BitNet b1.58 on MacBook Pro 16" (M4 Max, 48 GB)?

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 1 marca 2024

Yes — comfortably

Yes, comfortably — BitNet b1.58 3B at Q8_0 needs about 5.2 GB of the 36 GB usable on MacBook Pro 16" (M4 Max, 48 GB), leaving ~30.8 GB spare and running at ~61.6 tok/s (estimated).

Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~61.6 tok/s

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MacBook Pro 16" (M4 Max, 48 GB) — what it gives a model

Usable memory for models	36 GB
Memory bandwidth	546 GB/s
Form factor	Laptop
Operating system	macOS
Memory upgradeable	No — soldered
Price	$3,999 (lib/data/laptops.ts (street price), checked 2026-07-06)

BitNet b1.58 on MacBook Pro 16" (M4 Max, 48 GB): memory by quantization

Quant	Memory needed	Fits 36 GB?	Max context	Est. speed	Download
F16	8.3 GB	✓ Yes	—	~37.5 tok/s	6.6 GB
Q8_0	5.2 GB	✓ Yes	—	~61.6 tok/s	3.5 GB
Q6_K	4.4 GB	✓ Yes	—	~73.8 tok/s	2.7 GB
Q5_K_M	4 GB	✓ Yes	—	~81.2 tok/s	2.4 GB
Q4_K_M	3.7 GB	✓ Yes	—	~89.6 tok/s	2 GB
Q3_K_M	3.1 GB	✓ Yes	—	~108.9 tok/s	1.4 GB
Q2_K	2.8 GB	✓ Yes	—	~123.4 tok/s	1.1 GB

What to watch out for

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 M4 Max 48 GB limitations

Unified memory is soldered and cannot be upgraded after purchase.
macOS reserves a share of unified memory for the system, so not all 48 GB is available to a model.

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.
48 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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