Can I Run Llama 3.1 Family on Mac Studio (M3 Ultra, 256 GB)?

Superseded model. Llama 3.1 Family has been superseded by Llama 4. This page is kept for reference; the newer family is a better starting point. View Llama 4 →

Written by Jakub Rusinowski · Last updated July 23, 2024

Yes — comfortably

Yes, comfortably — Llama 3.1 8B Instruct at Q8_0 needs about 10.4 GB of the 192 GB usable on Mac Studio (M3 Ultra, 256 GB), leaving ~181.6 GB spare and running at ~43.2 tok/s (estimated), with room for about 131,072 tokens of context.

Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~43.2 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

Llama 3.1 Family on Mac Studio (M3 Ultra, 256 GB): memory by quantization

Quant	Memory needed	Fits 192 GB?	Max context	Est. speed	Download
F16	17.9 GB	✓ Yes	128K	~25.1 tok/s	16 GB
Q8_0	10.4 GB	✓ Yes	128K	~43.2 tok/s	8.5 GB
Q6_K	8.4 GB	✓ Yes	128K	~53.2 tok/s	6.6 GB
Q5_K_M	7.5 GB	✓ Yes	128K	~59.4 tok/s	5.7 GB
Q4_K_M	6.7 GB	✓ Yes	128K	~66.9 tok/s	4.8 GB
Q3_K_M	5.3 GB	✓ Yes	128K	~84.8 tok/s	3.4 GB
Q2_K	4.5 GB	✓ Yes	128K	~99.5 tok/s	2.6 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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