Written by Jakub Rusinowski · Last updated August 15, 2026
Moonshot AI's cutting-edge coding and agentic model series. Kimi K2.5, K2.6 and the coding-specialised K2.7 Code rank among the top models globally for coding tasks, multimodal understanding, and autonomous agent workflows. Built for developers who need a model that can reason, use tools, browse the web, write and debug code end-to-end.
| Licence | What it permits | Applies to |
|---|---|---|
Kimi Research | Research / non-commercial only Research / non-commercial only — this licence does NOT permit shipping a commercial product. | Kimi K2.5, Kimi K2.6 |
Custom Open-Weight | Commercial use permitted Weights are downloadable and commercial use is permitted, subject to the licence’s acceptable-use terms. | Kimi K2.5 1T (32B Active) |
Modified MIT | Commercial use permitted Commercial use permitted. No usage restrictions beyond attribution. | Kimi K2.7 Code |
| Kimi K2.5 | Min 20 GB VRAM · Q4_K_M · 128,000 ctx · ollama run hf.co/moonshotai/Kimi-K2.5-Instruct-Q4_K_M |
| Kimi K2.6 | Min 20 GB VRAM · Q4_K_M · 128,000 ctx · ollama run hf.co/moonshotai/Kimi-K2.6-Instruct-Q4_K_M |
| Kimi K2.5 1T (32B Active) | Min 605 GB VRAM · Q4_K_M · 200,000 ctx · ollama run hf.co/moonshotai/Kimi-K2.5 |
| Kimi K2.7 Code | Min 605 GB VRAM · INT4 (native) · 262,144 ctx · |
The cheapest GPU that runs Kimi K2.5 / K2.6 / K2.7 locally (min 20 GB VRAM) is the AMD Radeon RX 7900 XT (20 GB).
Install Ollama then run: ollama run hf.co/moonshotai/Kimi-K2.5-Instruct-Q4_K_M
Minimum VRAM: 20 GB. For best results use Q4_K_M quantization.
Kimi K2.5 / K2.6 / K2.7 needs about 20 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: Kimi K2.5 (20 GB, Q4_K_M); Kimi K2.6 (20 GB, Q4_K_M); Kimi K2.5 1T (32B Active) (605 GB, Q4_K_M); Kimi K2.7 Code (605 GB, INT4 (native)). On Apple Silicon, unified memory counts toward this requirement.
Yes — Kimi K2.5 / K2.6 / K2.7 runs on an RTX 4090 (24 GB) and other 24 GB cards such as the RTX 3090. Smaller variants also fit comfortably on 8–16 GB GPUs at Q4_K_M.
Q4_K_M is the best balance of quality and VRAM for Kimi K2.5 / K2.6 / K2.7 in most cases. Choose Q8_0 for near-lossless quality if you have spare VRAM, or smaller quants (Q3/Q2) only when memory is tight.
Install Ollama, then run: ollama run hf.co/moonshotai/Kimi-K2.5-Instruct-Q4_K_M. This downloads Kimi K2.5 / K2.6 / K2.7 and starts a local, OpenAI-compatible endpoint — no internet connection is needed after the initial download.