Can I Run Cogito v1 on MacBook Pro 16" (M4 Max, 128 GB)?

Written by Jakub Rusinowski · Last updated March 20, 2026

Yes — comfortably

Yes, comfortably — Cogito v1 70B at Q6_K needs about 60.9 GB of the 96 GB usable on MacBook Pro 16" (M4 Max, 128 GB), leaving ~35.1 GB spare and running at ~5 tok/s (estimated), with room for about 65,536 tokens of context.

Confidence: high · Recommended quantization: Q6_K · Estimated speed: ~5 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

Cogito v1 on MacBook Pro 16" (M4 Max, 128 GB): memory by quantization

Quant	Memory needed	Fits 96 GB?	Max context	Est. speed	Download
F16	143.5 GB	✗ No	—	—	140 GB
Q8_0	77.9 GB	✓ Yes	32K	~3.9 tok/s	74.4 GB
Q6_K	60.9 GB	✓ Yes	64K	~5 tok/s	57.4 GB
Q5_K_M	53.1 GB	✓ Yes	128K	~5.8 tok/s	49.6 GB
Q4_K_M	45.7 GB	✓ Yes	128K	~6.7 tok/s	42.3 GB
Q3_K_M	33.3 GB	✓ Yes	128K	~9.3 tok/s	29.8 GB
Q2_K	26.5 GB	✓ Yes	128K	~11.9 tok/s	23 GB

Which Cogito v1 sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Cogito v1 70B	45.7 GB	✓ Fits	~6.7 tok/s
Cogito v1 32B	22.3 GB	✓ Fits	~14.1 tok/s
Cogito v1 14B	10.9 GB	✓ Fits	~29.5 tok/s
Cogito v1 8B	6.7 GB	✓ Fits	~47.8 tok/s
Cogito v1 3B	3.6 GB	✓ Fits	~94.1 tok/s

What to watch out for

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 computed from this model's published attention configuration.

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