How to Setup Kimi-K2-Instruct-0905 on Copilot+ PC

The most rapid route to a local installation of this model is through WSL2.

Review and follow the instructions below.

The setup auto-streams the model assets (expect a multi-GB download).

The setup file includes a feature that instantly optimizes all configurations.

🖹 HASH-SUM: 94599936838f727ba942e2c1ac9ff4a5 | 📅 Updated on: 2026-06-28



  • Processor: Intel i7 / Ryzen 7 for heavy Quantized models
  • RAM: 64 GB to avoid OOM crashes on large contexts
  • Disk Space: at least 100 GB for multiple local LLM variants
  • Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration

The Kimi-K2-Instruct-0905 model represents a significant advancement in instruction‑following large language models, combining massive scale with refined reasoning capabilities. It was trained on a diverse corpus of over 2 trillion tokens, encompassing scientific papers, technical documentation, and curated instructional datasets to enhance its ability to interpret complex directives. The architecture leverages a transformer‑based design with a 10‑trillion parameter configuration, enabling rapid inference and low‑latency responses across multilingual tasks. In benchmark evaluations, the model achieves state‑of‑the‑art performance on reasoning, coding, and factual QA, often surpassing peers by a notable margin thanks to its instruction‑tuned optimization. A concise overview of its core specifications is provided below, allowing developers to quickly assess compatibility and performance for their applications.

Parameter Count 10 trillion
Training Tokens 2 trillion
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