Coding
How to deploy MiniMax-M2 on a GPU cloud
A 230B (MoE) model built for coding and agentic tool-use workflows. Full specs, license and use cases.
MiniMax-M2 size and hardware requirements
228.7B
Total parameters
Mixture-of-experts: 8 of 256 experts active per token (10B active parameters, 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
Run MiniMax-M2 with vLLM
From MiniMaxAI/MiniMax-M2's own vLLM deployment guide (8-GPU recipe).
SAFETENSORS_FAST_GPU=1 vllm serve \
MiniMaxAI/MiniMax-M2 --trust-remote-code \
--enable_expert_parallel --tensor-parallel-size 8 \
--enable-auto-tool-choice --tool-call-parser minimax_m2 \
--reasoning-parser minimax_m2_append_thinkSource: https://huggingface.co/MiniMaxAI/MiniMax-M2/raw/main/docs/vllm_deploy_guide.md
Deploy MiniMax-M2 on Aquanode
Aquanode has no one-click deploy template for MiniMax-M2; 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