MiniMax M2.7 230B-A10B — VRAM, Speed & Local Setup

Written by Jakub Rusinowski · Last updated August 15, 2026

Model libraryMiniMax M2.7 → MiniMax M2.7 230B-A10B

230B total / 10B active across 256 experts. Needs roughly 130 GB at Q4 — a multi-GPU workstation or a rented node. The headline constraint is legal rather than technical: the weights are freely downloadable but the license is non-commercial, so it is not a drop-in for business use the way an Apache or MIT model would be. Context window is recorded as null here: published figures disagree (200K in most sources, 192K in the vLLM recipe) and neither could be confirmed against a primary page from this environment.

MiniMax M2.7 230B-A10B needs about 140 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.

Specifications

Parameters230 Billion (10B active)
Context windowUnverified — sources disagree (192K–200K)
ArchitectureMixture-of-Experts (256 experts, 62 layers, RoPE)
ProviderMiniMax
LicenceNon-Commercial (commercial use requires separate agreement)
Specified atQ4_K_M
System RAM256 GB
Record updated2026-08-15

Licence

CC-BY-NC-4.0research / non-commercial only. Research / non-commercial only — this licence does NOT permit shipping a commercial product.

VRAM and Speed by Quantization

Modelled on a reference NVIDIA RTX 4090 (24 GB), with no KV cache (this record has no published architecture). Speed figures are ESTIMATES from the memory-bandwidth roofline described on the methodology page, not benchmarks we ran — rows marked measured come from published or reader-submitted runs. VRAM here includes the KV cache, so it reads higher than the headline figure above, which does not.

QuantWeightsVRAM neededEst. speedFit on 24 GB
Q2_K75.6 GB76.4 GBWon't fit
Q3_K_M98.0 GB98.8 GBWon't fit
Q4_K_M138.9 GB139.7 GBWon't fit
Q5_K_M163.0 GB163.8 GBWon't fit
Q6_K188.6 GB189.4 GBWon't fit
Q8_0244.4 GB245.2 GBWon't fit
F16460.0 GB460.8 GBWon't fit

Want the memory numbers alone, at every quantization level and your own context length? Use the MiniMax M2.7 230B-A10B VRAM calculator.

Buy This HardwareApple Mac Studio M2 Ultra — 192 GB VRAM · 60 W board powerDeploy in the Cloud NowRunPod

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Recommended GPU

The cheapest catalogued GPU that runs MiniMax M2.7 230B-A10B is the Apple M2 Ultra (192 GB).

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Apple Mac Studio M2 Ultra
192 GB VRAM · 60 W board power
2026 prices are volatile — check the current listing.
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How to Run MiniMax M2.7 230B-A10B

Install Ollama, then run:

ollama run minimax-m2-7

Weights on Hugging Face: MiniMaxAI/MiniMax-M2.7.

Best for: agentic tasks, reasoning, research, non commercial.

Can I Run MiniMax M2.7 230B-A10B on My GPU?

MiniMax M2.7 230B-A10B — Frequently Asked Questions

How much VRAM does MiniMax M2.7 230B-A10B need?
About 140 GB at Q4_K_M — quantized weights plus framework overhead, before any KV cache. The cache grows with context length and is added on top; the table above folds it in. Apple Silicon counts unified memory toward the same figure.
Does MiniMax M2.7 230B-A10B run on an RTX 4090 (24 GB)?
No. MiniMax M2.7 230B-A10B needs about 140 GB at Q4_K_M, more than a single RTX 4090's 24 GB. It needs a larger card, several GPUs, or Apple Silicon with enough unified memory — or it runs with part of the weights offloaded to system RAM, which is much slower.
How do I run MiniMax M2.7 230B-A10B locally?
Install Ollama and run `ollama run minimax-m2-7`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.

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