Can I Run DeepSeek-OCR on Mac Studio (M2 Ultra, 192 GB)?

Written by Jakub Rusinowski · Last updated September 11, 2026

Yes — comfortably

Yes, comfortably — DeepSeek-OCR 3B at Q8_0 needs about 4.8 GB of the 144 GB usable on Mac Studio (M2 Ultra, 192 GB), leaving ~139.2 GB spare and running at ~187.3 tok/s (estimated), with room for about 8,192 tokens of context.

Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~187.3 tok/s

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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

DeepSeek-OCR on Mac Studio (M2 Ultra, 192 GB): memory by quantization

Quant	Memory needed	Fits 144 GB?	Max context	Est. speed	Download
F16	7.6 GB	✓ Yes	8K	~152.5 tok/s	6 GB
Q8_0	4.8 GB	✓ Yes	8K	~187.3 tok/s	3.2 GB
Q6_K	4.1 GB	✓ Yes	8K	~199.1 tok/s	2.5 GB
Q5_K_M	3.8 GB	✓ Yes	8K	~205 tok/s	2.1 GB
Q4_K_M	3.5 GB	✓ Yes	8K	~210.9 tok/s	1.8 GB
Q3_K_M	2.9 GB	✓ Yes	8K	~221.7 tok/s	1.3 GB
Q2_K	2.6 GB	✓ Yes	8K	~228.1 tok/s	1 GB

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 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Superseded by the M3 Ultra, which is why it is often the better used buy for large-model work.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 inferred from the parameter count because this model does not publish its full attention configuration.

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