Setup Kimi-K2-Instruct-0905 on Your PC with 1M Context

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

Refer to the instructions below to proceed.

The client handles the setup, pulling gigabytes of data automatically.

Once launched, the wizard detects your specs to configure the model for maximum efficiency.

📊 File Hash: efd5c23e91c05110034eaec4ca89c43f — Last update: 2026-06-26 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: 32 GB highly recommended for 26B+ GGUF models Disk Space: at least 100 GB for multiple local LLM variants Graphics: stable 30+ tk/s at 4-bit quantization on medium setup

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.

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