Nature’s Moss Art

Zero-Click Run Kimi-K2-Instruct-0905 Locally via Ollama 2 Easy Build

The fastest way to get this model running locally is via Optional Features.

Execute the commands and steps outlined below.

The setup auto-downloads all needed files (several GBs).

The engine benchmarks your hardware to apply the most effective operational mode.

🔍 Hash-sum: b4bdee51e1607ee13b2dd0570162c8a1 | 🕓 Last update: 2026-06-27



  • Processor: 6-core 3.5 GHz minimum required
  • RAM: fast 5600MHz+ required to avoid memory bottlenecks
  • Disk Space: free: 80 GB on system drive for scratch space
  • Graphics: 12 GB VRAM minimum required for basic quantization

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