2026-07-03

Kimi-K2.6 Quantized GGUF Step-by-Step

The shortest path to running this model is by activating Hyper-V features.

Make sure to follow the instructions below.

An automated background process downloads all required large-scale files.

The program scans your VRAM and RAM to seamlessly apply optimal configurations.

🖹 HASH-SUM: 2b84525e7be469213f13156c4f5b5743 | 📅 Updated on: 2026-06-28
  • CPU: multi-threading optimized for fast prompt processing
  • RAM: high-speed DDR5 memory preferred for CPU offloading
  • Storage: extra room for future model updates and datasets
  • GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats

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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