Can I Run MiMo-V2.5 on Mac Studio (M3 Ultra, 256 GB)?

Written by Jakub Rusinowski · Last updated September 6, 2026

Yes, but it is tight

Yes, but it is tight — MiMo-V2.5 310B at Q4_K_M needs about 191.9 GB of the 192 GB usable on Mac Studio (M3 Ultra, 256 GB), leaving only ~0.1 GB before the runtime starts swapping. Expect ~36.4 tok/s (estimated), with room for about 8,192 tokens of context.

Confidence: low · Recommended quantization: Q4_K_M · Estimated speed: ~36.4 tok/s

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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

MiMo-V2.5 on Mac Studio (M3 Ultra, 256 GB): memory by quantization

Quant	Memory needed	Fits 192 GB?	Max context	Est. speed	Download
F16	624.7 GB	✗ No	—	—	620 GB
Q8_0	334.1 GB	✗ No	—	—	329.4 GB
Q6_K	258.9 GB	✗ No	—	—	254.2 GB
Q5_K_M	224.4 GB	✗ No	—	—	219.7 GB
Q4_K_M	191.9 GB	✓ Yes	8K	~36.4 tok/s	187.2 GB
Q3_K_M	136.8 GB	✓ Yes	64K	~46.3 tok/s	132.1 GB
Q2_K	106.6 GB	✓ Yes	128K	~54.6 tok/s	101.9 GB

Superseded model. MiMo-V2.5 has been superseded by MiMo-V2.6. This page is kept for reference; the newer family is a better starting point. View MiMo-V2.6-Flash →

Which MiMo-V2.5 sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
MiMo-V2.5-Pro 1T	610.3 GB	✗ Too large	—
MiMo-V2.5 310B	191.9 GB	✓ Fits	~36.4 tok/s

What to watch out for

Only ~0.1 GB of headroom at Q4_K_M: a longer context or a second application can push this into swapping.
1 larger variant of MiMo-V2.5 does not fit and would need CPU offload or different hardware.
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 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 inferred from the parameter count because this model does not publish its full attention configuration.

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