Install Kimi-K2-Instruct-0905 on AMD/Nvidia GPU Easy Build

If you need a near-instant local setup, just fetch files via a basic curl request.

Use the instructions provided below to complete the setup.

The tool automatically synchronizes and downloads the model database.

You don’t need to tweak anything; the installer picks the highest performing setup.

📎 HASH: 5a5c31da9ef4b7419f97dbadce0c68b6 | Updated: 2026-06-27



  • CPU: AVX2/AVX-512 instruction set required for llama.cpp
  • RAM: 32 GB or higher for smooth 32k context lengths
  • Disk: 150+ GB for high-context vector database storage
  • Graphics: TensorRT-LLM / vLLM inference engine compatible chip

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
  1. Script downloading visual document layout analytical models for local OCR parsing
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  3. Downloader pulling micro-parameter language files for instantaneous automated replies
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  5. Setup utility configuring high-speed semantic index models for local RAG database matrix pools
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  7. Downloader pulling specialized textual inversion files for photographic facial fixes
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  9. Installer deploying local real-time text-to-speech channels via ChatTTS engines
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