Can I Run Llama 3.3 on Mac Studio (M2 Ultra, 192 GB)?

Superseded model. Llama 3.3 has been superseded by Llama 4.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 December 8, 2024

Yes — comfortably

Yes, comfortably — Llama 3.3 70B Instruct at Q8_0 needs about 77.9 GB of the 144 GB usable on Mac Studio (M2 Ultra, 192 GB), leaving ~66.1 GB spare and running at ~5.7 tok/s (estimated), with room for about 131,072 tokens of context.

Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~5.7 tok/s

Mac Studio (M2 Ultra, 192 GB) — what it gives a model

Usable memory for models	144 GB
Memory bandwidth	800 GB/s
Form factor	Desktop
Operating system	macOS
Memory upgradeable	No — soldered

Llama 3.3 on Mac Studio (M2 Ultra, 192 GB): memory by quantization

Quant	Memory needed	Fits 144 GB?	Max context	Est. speed	Download
F16	143.5 GB	✓ Yes	8K	~3.1 tok/s	140 GB
Q8_0	77.9 GB	✓ Yes	128K	~5.7 tok/s	74.4 GB
Q6_K	60.9 GB	✓ Yes	128K	~7.3 tok/s	57.4 GB
Q5_K_M	53.1 GB	✓ Yes	128K	~8.4 tok/s	49.6 GB
Q4_K_M	45.7 GB	✓ Yes	128K	~9.8 tok/s	42.3 GB
Q3_K_M	33.3 GB	✓ Yes	128K	~13.5 tok/s	29.8 GB
Q2_K	26.5 GB	✓ Yes	128K	~17.1 tok/s	23 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 M2 Ultra 192 GB limitations

Superseded by the M3 Ultra, which is why it is often the better used buy for large-model work.
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.
192 GB unified memory at 800 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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