Model ID
MiniMaxAI/MiniMax-M2.7
Capabilities
MiniMax M2.7
MiniMax M2.7 is a large-scale Mixture-of-Experts (MoE) language model developed by MiniMax for coding, agentic workflows, and enterprise automation tasks. It features a 229B parameter architecture with ~10B active parameters, built on a 62-layer transformer with 48 attention heads and a 3,072 hidden size. It employs a 256-expert MoE architecture with 8 experts per token and supports up to a 200K context window. It is optimized for autonomous agent teams, iterative self-improvement, and complex real-world task execution.
About this model
MiniMax M2.7 is a large-scale Mixture-of-Experts (MoE) language model developed by MiniMax for coding, agentic workflows, and enterprise automation tasks. It features a 229B parameter architecture with ~10B active parameters, built on a 62-layer transformer with 48 attention heads and a 3,072 hidden size. It employs a 256-expert MoE architecture with 8 experts per token and supports up to a 200K context window. It is optimized for autonomous agent teams, iterative self-improvement, and complex real-world task execution.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (MiniMaxAI/MiniMax-M2.7). Released under Modified MIT. Further reading: Blog
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Hardware
MI325X
2,048 GB
B200
1,440 GB
Engine
vLLM
SGLang
Llama.cpp
Deployment code
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Performance under load
Tokens/s (total)
Benchmarks measured by Vultr on MI325X · vLLM. Throughput and latency vary with concurrency, input length and engine configuration, so treat these as a comparison baseline, not a service guarantee.
AMD Instinct MI325X
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