Kimi K2.5 / K2.6 / K2.7 — Local AI Model by Moonshot AI

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 6 września 2026

Przełom Moonshot AI z lutego 2026. Kimi K2.5 ma 1 bilion parametrów (32B aktywnych, 384 ekspertów) i osiągnął najwyższy wynik w historii HumanEval: 99,0. Wytrenowany na 15 bilionach tokenów wizyjnych i tekstowych od podstaw — wizja to kluczowa funkcja, nie dodatek.

Licence

LicenceWhat it permitsApplies to
Modified MITCommercial use permitted
Commercial use permitted. No usage restrictions beyond attribution.
Kimi K2.5, Kimi K2.6, Kimi K2.7 Code
Custom Open-WeightCommercial use permitted
Weights are downloadable and commercial use is permitted, subject to the licence’s acceptable-use terms.
Kimi K2.5 1T (32B Active)

Hardware Requirements

Kimi K2.5Min 605 GB VRAM · Q4_K_M · 256,000 ctx · ollama run hf.co/moonshotai/Kimi-K2.5-Instruct-Q4_K_M
Kimi K2.6Min 605 GB VRAM · Q4_K_M · 256,000 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 · 256,000 ctx · ollama run hf.co/moonshotai/Kimi-K2.5
Kimi K2.7 CodeMin 605 GB VRAM · INT4 (native) · 262,144 ctx ·

How to Run Locally

Install Ollama then run: ollama run hf.co/moonshotai/Kimi-K2.5-Instruct-Q4_K_M

Minimum VRAM: 605 GB. For best results use Q4_K_M quantization.

Kimi K2.5 / K2.6 / K2.7 — Frequently Asked Questions

How much VRAM does Kimi K2.5 / K2.6 / K2.7 need?

Kimi K2.5 / K2.6 / K2.7 needs about 605 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: Kimi K2.5 (605 GB, Q4_K_M); Kimi K2.6 (605 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.

Can I run Kimi K2.5 / K2.6 / K2.7 on an RTX 4090 (24 GB)?

Kimi K2.5 / K2.6 / K2.7's smallest variant needs about 605 GB, which exceeds a single RTX 4090 (24 GB). Use multiple GPUs, a higher-VRAM card, or Apple Silicon with large unified memory.

What quantization should I use for Kimi K2.5 / K2.6 / K2.7?

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.

How do I run Kimi K2.5 / K2.6 / K2.7 with Ollama?

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.