Model ID
MiniMaxAI/MiniMax-M2.1
Capabilities
MiniMax M2.1
MiniMax M2.1 is a large-scale Mixture-of-Experts (MoE) language model developed by MiniMax focused on real-world agent reliability. 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, utilizing 256 experts with 8 experts activated per token. It supports a 200K token context window, making it well suited for long-horizon workflows, composite instructions, and stable performance across agent frameworks and production environments.
About this model
MiniMax M2.1 is a large-scale Mixture-of-Experts (MoE) language model developed by MiniMax focused on real-world agent reliability. 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, utilizing 256 experts with 8 experts activated per token. It supports a 200K token context window, making it well suited for long-horizon workflows, composite instructions, and stable performance across agent frameworks and production environments.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (MiniMaxAI/MiniMax-M2.1). Released under Modified MIT. Further reading: Blog
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Hardware
MI325X
2,048 GB
B200
1,440 GB
Engine
vLLM
SGLang
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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