The most rapid route to a local installation of this model is through WSL2.
Check out the detailed setup guide below to begin.
The client handles the setup, pulling gigabytes of data automatically.
The smart installation system will instantly find the perfect configuration.
🔍 Hash-sum: e93bb72f601838403cfe2cd6de46c1e7 | 🕓 Last update: 2026-06-27
- Processor: next-gen chip for heavy context processing
- RAM: fast 5600MHz+ required to avoid memory bottlenecks
- Storage:100 GB free space for HuggingFace cache folder
- GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats
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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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