LLM
How to deploy MiniMax-M2.7 on a GPU cloud
A 229B (MoE) language model for chat and instruction-following. Full specs, license and use cases.
MiniMax-M2.7 size and hardware requirements
228.7B
Total parameters
Mixture-of-experts: 8 of 256 experts active per token (exact active-parameter count not stated on the model card)
Active parameters
F8_E4M3
Published precision
255.6 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP8 (native) | 213.0 GB | 255.6 GB | RTX 4080 Super | 8 | $3.06/hr |
| INT4 (quantized) | 106.5 GB | 127.8 GB | RTX 3090 | 6 | $0.882/hr |
How to run MiniMax-M2.7
Run MiniMax-M2.7 with vLLM
Generic example, not from the model's own docs: adjust flags (quantization, context length, parallelism) for your setup.
vllm serve MiniMaxAI/MiniMax-M2.7 --tensor-parallel-size 8Run MiniMax-M2.7 with GGUF quantizations
Prebuilt GGUF weights published at unsloth/MiniMax-M2.7-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf unsloth/MiniMax-M2.7-GGUFDeploy MiniMax-M2.7 on Aquanode
Aquanode has no one-click deploy template for MiniMax-M2.7; you install the inference engine yourself with the commands below. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch a bare GPU pod sized to the requirement above (8× RTX 4080 Super or larger).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch a GPU pod