Can I Run InternLM 3 on MacBook Pro 16" (M4 Max, 128 GB)?

Written by Jakub Rusinowski · Last updated January 15, 2025

Yes — comfortably

Yes, comfortably — InternLM 3 20B Instruct at Q8_0 needs about 23.6 GB of the 96 GB usable on MacBook Pro 16" (M4 Max, 128 GB), leaving ~72.4 GB spare and running at ~13 tok/s (estimated), with room for about 32,768 tokens of context.

Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~13 tok/s

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MacBook Pro 16" (M4 Max, 128 GB) — what it gives a model

Usable memory for models	96 GB
Memory bandwidth	546 GB/s
Form factor	Laptop
Operating system	macOS
Memory upgradeable	No — soldered

InternLM 3 on MacBook Pro 16" (M4 Max, 128 GB): memory by quantization

Quant	Memory needed	Fits 96 GB?	Max context	Est. speed	Download
F16	42.4 GB	✓ Yes	32K	~7.2 tok/s	40 GB
Q8_0	23.6 GB	✓ Yes	32K	~13 tok/s	21.3 GB
Q6_K	18.8 GB	✓ Yes	32K	~16.5 tok/s	16.4 GB
Q5_K_M	16.6 GB	✓ Yes	32K	~18.9 tok/s	14.2 GB
Q4_K_M	14.5 GB	✓ Yes	32K	~21.7 tok/s	12.1 GB
Q3_K_M	10.9 GB	✓ Yes	32K	~29.3 tok/s	8.5 GB
Q2_K	9 GB	✓ Yes	32K	~36.2 tok/s	6.6 GB

Which InternLM 3 sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
InternLM 3 20B Instruct	14.5 GB	✓ Fits	~21.7 tok/s
InternLM 3 8B Instruct	6.5 GB	✓ Fits	~46.7 tok/s

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 128 GB limitations

Unified memory is soldered — the 128 GB decision is permanent and cannot be upgraded later.
Sustained decode speed on battery is materially below the plugged-in figures quoted here.

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 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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