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

作者： Jakub Rusinowski · 最后更新： 2026年8月15日

Moonshot AI 2026年2月的突破之作。Kimi K2.5拥有1万亿总参数（32B活跃，384专家），创下HumanEval历史最高分99.0。在15万亿混合视觉和文本token上训练。具备独特的智能体群集架构。

Licence

LicenceWhat it permitsApplies to
Kimi ResearchResearch / non-commercial only
Research / non-commercial only — this licence does NOT permit shipping a commercial product.		Kimi K2.5, Kimi K2.6
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)
Modified MITCommercial use permitted
Commercial use permitted. No usage restrictions beyond attribution.		Kimi K2.7 Code

Hardware Requirements

Kimi K2.5Min 20 GB VRAM · Q4_K_M · 128,000 ctx · ollama run hf.co/moonshotai/Kimi-K2.5-Instruct-Q4_K_M
Kimi K2.6Min 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 CodeMin 605 GB VRAM · INT4 (native) · 262,144 ctx ·

Recommended GPU

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).

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AMD Radeon RX 7900 XT 20GB
20 GB VRAM · 315 W board power
2026年价格波动较大——请以当前商品页价格为准。
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How to Run Locally

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 — Frequently Asked Questions

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

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.

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

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.

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.