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

Name: LLM Configurator — GPU VRAM Checker
Author: LLM Configurator

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 10 lipca 2026

Moonshot AI's cutting-edge coding and agentic model series. Kimi K2.5 and K2.6 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.

Hardware Requirements

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`

Recommended GPU

The cheapest GPU that runs Kimi K2.5 / K2.6 locally (min 20 GB VRAM) is the AMD Radeon RX 7900 XT (20 GB).

Ujawnienie afiliacyjne: Niektóre odnośniki na tej stronie to linki afiliacyjne — jeśli dokonasz zakupu za ich pośrednictwem, LLM Configurator może otrzymać prowizję bez dodatkowych kosztów dla Ciebie. Jako uczestnik programu Amazon Associates, LLM Configurator zarabia na kwalifikujących się zakupach.

AMD Radeon RX 7900 XT 20GB

Sugerowana cena premierowa: $899

Ceny w 2026 są niestabilne — sprawdź aktualną ofertę.

Sprawdź cenę na Amazon

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 — Frequently Asked Questions

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

Kimi K2.5 / K2.6 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). On Apple Silicon, unified memory counts toward this requirement.

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

Yes — Kimi K2.5 / K2.6 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?

Q4_K_M is the best balance of quality and VRAM for Kimi K2.5 / K2.6 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 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 and starts a local, OpenAI-compatible endpoint — no internet connection is needed after the initial download.