Moonshot AI
Kimi K2.5 (1000B parameters) requires approximately 620.4 GB of VRAM with Q4_K_M quantization. As a Mixture of Experts model with 32B active parameters, it uses less memory than its total parameter count suggests. For the best balance of quality and speed, we recommend hardware with at least 714 GB of VRAM.
Get started
— copy & paste to run locallyCopy-paste commands to run Kimi K2.5 on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "moonshotai/Kimi-K2.5" \
--hf-file "Kimi-K2.5-Q4_K_M.gguf" \
-c 4096 -ngl 99Quick specs
About this model
Related models
Quantization options
No hardware detected — fit column shows raw VRAM estimates
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 390.0 GB | Low | — |
Q3_K_S | 3 | 490.0 GB | Low | — |
NVFP4 | 4 | 560.0 GB | Medium | — |
Q4_K_M | 4 | 610.0 GB | Medium | — |
Q5_K_M | 5 | 720.0 GB | High | — |
Q6_K | 6 | 820.0 GB | High | — |
Q8_0 | 8 | 1070.0 GB | Very High | — |
F16 | 16 | 2050.0 GB | Maximum | — |
Quality benchmarks
Coding
Reasoning
General
Source: official · 2026-01-01
Hardware compatibility
Computing compatibility...
Memory breakdown
Frequently asked questions
Kimi K2.5 (1000B parameters) requires approximately 620.4 GB of VRAM with Q4_K_M quantization. Lower quantizations like Q4_K_M use less memory but may reduce quality.
The recommended quantization for Kimi K2.5 is Q4_K_M, which offers the best balance between model quality and memory efficiency. Higher quantizations preserve more quality but require more VRAM.
Yes, Kimi K2.5 is well-suited for chat as well as coding, reasoning, vision. It was designed with these use cases in mind.
See also