Model ID
moonshotai/Kimi-K2.6
Capabilities
Kimi K2.6
Kimi K2.6 is a native multimodal Mixture-of-Experts model designed for long-horizon coding, agentic workflows, and large-scale autonomous task orchestration, succeeding Kimi K2.5. It features 1T total parameters with ~32B active, 61 layers, 7168 hidden size, and 64 attention heads, activating 8 of 384 experts plus 1 shared expert per token. The model supports a 256K context window with MLA attention and YARN RoPE scaling. It integrates a 400M-parameter MoonViT vision encoder and enables large-scale agent swarms for parallel task execution.
About this model
Kimi K2.6 is a native multimodal Mixture-of-Experts model designed for long-horizon coding, agentic workflows, and large-scale autonomous task orchestration, succeeding Kimi K2.5. It features 1T total parameters with ~32B active, 61 layers, 7168 hidden size, and 64 attention heads, activating 8 of 384 experts plus 1 shared expert per token. The model supports a 256K context window with MLA attention and YARN RoPE scaling. It integrates a 400M-parameter MoonViT vision encoder and enables large-scale agent swarms for parallel task execution.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (moonshotai/Kimi-K2.6). 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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