MiniMax M2.7 — local AI model by MiniMax

Written by Jakub Rusinowski · Last updated

MiniMax's March 2026 agentic MoE: 230B total parameters with 10B active per token across 256 experts — a 4.3% activation rate — over 62 layers, using multi-head causal self-attention with RoPE. Weights are published openly on Hugging Face and GitHub, so this is self-hostable rather than API-only, though the scale still implies a multi-GPU box. NVIDIA has upstreamed fused QK RMS Norm and FP8 MoE kernels from TensorRT-LLM for it. ⚠️ Released under a NON-COMMERCIAL license — commercial deployment requires a separate agreement with MiniMax.

Variants

The smallest MiniMax M2.7 variant needs about 140 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache.

ModelVRAM
MiniMax M2.7 230B-A10B →
230B (10B active)
~139.7 GB

Memory is quantized weights plus overhead at Q4_K_M, from the same engine as the GPU & VRAM checker.

How to run MiniMax M2.7 locally

Install Ollama, then pull the tag.

ollama run minimax-m2-7

Pick a size above for its own VRAM figure, speed estimate and install command.

Licence

CC-BY-NC-4.0Research / non-commercial only

Research / non-commercial only — this licence does NOT permit shipping a commercial product.

Applies to: MiniMax M2.7 230B-A10B

Recommended GPU

The cheapest catalogued GPU that runs MiniMax M2.7 locally (min 140 GB VRAM) 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.

Can I run MiniMax M2.7 on my GPU?

MiniMax M2.7 — frequently asked questions

How much VRAM does MiniMax M2.7 need?

MiniMax M2.7 needs about 140 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: MiniMax M2.7 230B-A10B (140 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.

Can I run MiniMax M2.7 on an RTX 4090 (24 GB)?

MiniMax M2.7's smallest variant needs about 140 GB, which exceeds a single RTX 4090 (24 GB). Use multiple GPUs, a higher-VRAM card, or Apple Silicon with large unified memory.

What quantization should I use for MiniMax M2.7?

Q4_K_M is the best balance of quality and VRAM for MiniMax M2.7 in most cases. Choose Q8_0 for near-lossless quality if you have spare VRAM, or smaller quants (Q3/Q2) only when memory is tight.

How do I run MiniMax M2.7 with Ollama?

MiniMax M2.7 has no local Ollama tag — the published tag is cloud-hosted, so running it sends your prompts to a hosted GPU rather than your own machine.