Can I Run Nemotron 70B on Mac Studio (M3 Ultra, 256 GB)?

Superseded model. Nemotron 70B has been superseded by Nemotron 3 Super. This page is kept for reference; the newer family is a better starting point. View Nemotron 3 Super →

Written by Jakub Rusinowski · Last updated October 15, 2024

Yes — comfortably

Yes, comfortably — Nemotron 70B Instruct at Q8_0 needs about 78.5 GB of the 192 GB usable on Mac Studio (M3 Ultra, 256 GB), leaving ~113.5 GB spare and running at ~5.8 tok/s (estimated), with room for about 65,536 tokens of context.

Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~5.8 tok/s

Mac Studio (M3 Ultra, 256 GB) — what it gives a model

Usable memory for models	192 GB
Memory bandwidth	819 GB/s
Form factor	Desktop
Operating system	macOS
Memory upgradeable	No — soldered

Nemotron 70B on Mac Studio (M3 Ultra, 256 GB): memory by quantization

Quant	Memory needed	Fits 192 GB?	Max context	Est. speed	Download
F16	144.7 GB	✓ Yes	64K	~3.1 tok/s	141.2 GB
Q8_0	78.5 GB	✓ Yes	64K	~5.8 tok/s	75 GB
Q6_K	61.4 GB	✓ Yes	64K	~7.4 tok/s	57.9 GB
Q5_K_M	53.5 GB	✓ Yes	64K	~8.5 tok/s	50 GB
Q4_K_M	46.1 GB	✓ Yes	64K	~9.9 tok/s	42.6 GB
Q3_K_M	33.6 GB	✓ Yes	64K	~13.7 tok/s	30.1 GB
Q2_K	26.7 GB	✓ Yes	64K	~17.3 tok/s	23.2 GB

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.

Mac Studio M3 Ultra 256 GB limitations

The largest single-box unified memory pool available, but at roughly half the memory bandwidth of a high-end discrete GPU — very large models load, then run slowly.
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.
256 GB unified memory at 819 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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