Model ID
moonshotai/Kimi-K2-Thinking
Capabilities
Kimi K2 Thinking
Kimi K2 Thinking is a reasoning-focused Mixture-of-Experts (MoE) large language model, designed as a step-by-step thinking agent capable of dynamically invoking tools during reasoning. The model features a 1T parameter architecture with 32B activated parameters, 61 transformer layers, 64 attention heads, and 384 experts (8 experts per token). It supports a 256K token context window and is optimized for deep reasoning and long-horizon autonomous workflows. The model employs native INT4 quantization with Quantization-Aware Training (QAT) to reduce inference latency and memory usage while maintaining strong performance.
About this model
Kimi K2 Thinking is a reasoning-focused Mixture-of-Experts (MoE) large language model, designed as a step-by-step thinking agent capable of dynamically invoking tools during reasoning. The model features a 1T parameter architecture with 32B activated parameters, 61 transformer layers, 64 attention heads, and 384 experts (8 experts per token). It supports a 256K token context window and is optimized for deep reasoning and long-horizon autonomous workflows. The model employs native INT4 quantization with Quantization-Aware Training (QAT) to reduce inference latency and memory usage while maintaining strong performance.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (moonshotai/Kimi-K2-Thinking). Released under Modified MIT. Further reading: Paper · 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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