Kimi-K2.6 with Native FP4 Windows
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Kimi-K2.6 with Native FP4 Windows

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Kimi-K2.6 with Native FP4 Windows

Running this model locally is fastest when deployed through Docker.

Follow the guidelines below to continue.

1-click setup: the app automatically fetches the large weight files.

You don’t need to tweak anything, as the installer will automatically pick the highest performing setup for you.

???? Build Hash: 80cf10d3d7d557f70321c1b33773caf6 • ???? 2026-06-26
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  • Processor: next-gen chip for heavy context processing
  • RAM: 32 GB highly recommended for 26B+ GGUF models
  • Disk Space: free: 80 GB on system drive for scratch space
  • GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference

Kimi-K2.6 is a next‑generation language model that builds upon the successes of its predecessors with notable improvements in reasoning and multilingual capabilities. It employs a refined transformer architecture featuring sparse attention mechanisms that reduce computational load while preserving long‑range dependencies. The model was trained on an extensive corpus of over 5 trillion tokens, encompassing code, scientific literature, and diverse conversational data. With a parameter count of 180 billion and a context window of 8 K tokens, Kimi-K2.6 achieves state‑of‑the‑art performance across benchmark suites. The model specifications are summarized in the table below:

Parameters 180 B
Context Length 8 K tokens
Training Tokens 5 trillion
Architecture Transformer with sparse attention
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