Can I Run Nemotron Cascade 2 on Mac Studio (M4 Max, 64 GB)?

Written by Jakub Rusinowski · Last updated September 11, 2026

Yes, but it is tight

Yes, but it is tight — Nemotron Cascade 2 70B at Q4_K_M needs about 45.4 GB of the 48 GB usable on Mac Studio (M4 Max, 64 GB), leaving only ~2.6 GB before the runtime starts swapping. Expect ~6.7 tok/s (estimated), with room for about 16,384 tokens of context.

Confidence: low · Recommended quantization: Q4_K_M · Estimated speed: ~6.7 tok/s

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Mac Studio (M4 Max, 64 GB) — what it gives a model

Usable memory for models	48 GB
Memory bandwidth	546 GB/s
Form factor	Desktop
Operating system	macOS
Memory upgradeable	No — soldered

Nemotron Cascade 2 on Mac Studio (M4 Max, 64 GB): memory by quantization

Quant	Memory needed	Fits 48 GB?	Max context	Est. speed	Download
F16	143.2 GB	✗ No	—	—	140 GB
Q8_0	77.6 GB	✗ No	—	—	74.4 GB
Q6_K	60.6 GB	✗ No	—	—	57.4 GB
Q5_K_M	52.8 GB	✗ No	—	—	49.6 GB
Q4_K_M	45.4 GB	✓ Yes	16K	~6.7 tok/s	42.3 GB
Q3_K_M	33 GB	✓ Yes	32K	~9.4 tok/s	29.8 GB
Q2_K	26.2 GB	✓ Yes	64K	~11.9 tok/s	23 GB

Which Nemotron Cascade 2 sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Nemotron Cascade 2 70B	45.4 GB	✓ Fits	~6.7 tok/s
Nemotron-Cascade 2 30B-A3B	21.7 GB	✓ Fits	~76.4 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.

Mac Studio M4 Max 64 GB limitations

Memory is soldered; the configuration chosen at purchase is permanent.

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.
64 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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