Kimi K2.5 — local AI model by Moonshot AI
Written by Jakub Rusinowski · Last updated
Moonshot AI's 1-trillion parameter MoE flagship with 384 experts and a unique Agent Swarm architecture. Powers Cursor's Composer 2 IDE feature. Leads multiple coding and reasoning benchmarks including 93.33% on AIME 2026 and best Pass@1 on SWE-rebench. With 32B active parameters per token, requires datacenter infrastructure for self-hosting.
Variants
The smallest Kimi K2.5 variant needs about 605 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache.
| Model | VRAM at Q4 | VRAM | Context | Run it |
|---|---|---|---|---|
| Kimi K2.5 1T 1T | ~604.6 GB | 256,000 | ollama run kimi-k2-5 |
Memory is quantized weights plus overhead at Q4_K_M, from the same engine as the GPU & VRAM checker.
How to run Kimi K2.5 locally
Install Ollama, then pull the tag.
ollama run kimi-k2-5Pick a size above for its own VRAM figure, speed estimate and install command.
Licence
Weights are downloadable and commercial use is permitted, subject to the licence’s acceptable-use terms.
Applies to: Kimi K2.5 1TCan I run Kimi K2.5 on my GPU?
Kimi K2.5 — frequently asked questions
How much VRAM does Kimi K2.5 need?
Kimi K2.5 needs about 605 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: Kimi K2.5 1T (605 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.
Can I run Kimi K2.5 on an RTX 4090 (24 GB)?
Kimi K2.5'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?
Q4_K_M is the best balance of quality and VRAM for Kimi K2.5 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 with Ollama?
Kimi K2.5 has no local Ollama tag — the published tag is cloud-hosted, so running it sends your prompts to a hosted GPU rather than your own machine.