Kimi K2.5 — VRAM, Speed & Local Setup

Written by Jakub Rusinowski · Last updated September 6, 2026

Model library → Kimi K2.5 / K2.6 / K2.7 → Kimi K2.5

Moonshot AI's January 2026 flagship: a 1-trillion-parameter MoE that routes 8 of 384 experts per token, so it costs about what a 32B dense model costs to serve — but every one of those 1T weights has to be resident. Native multimodal via the 400M MoonViT encoder, 256K context, Modified MIT. Scores 76.8% on SWE-bench Verified. Self-hosting is datacenter-class: ~605 GB at Q4_K_M.

Kimi K2.5 needs about 605 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.

Specifications

Parameters	1 Trillion (32B active)
Context window	256,000
Architecture	MoE (384 experts, 8 active, MLA) + MoonViT vision
Provider	Moonshot AI
Licence	Modified MIT
Specified at	Q4_K_M
System RAM	768 GB
Record updated	2026-09-06

Licence

Modified MIT — commercial use permitted. Commercial use permitted. No usage restrictions beyond attribution.

VRAM and Speed by Quantization

Modelled on a reference NVIDIA RTX 4090 (24 GB), with no KV cache (this record has no published architecture). Speed figures are ESTIMATES from the memory-bandwidth roofline described on the methodology page, not benchmarks we ran — rows marked measured come from published or reader-submitted runs. VRAM here includes the KV cache, so it reads higher than the headline figure above, which does not.

Quant	Weights	VRAM needed	Est. speed	Fit on 24 GB
Q2_K	328.8 GB	329.6 GB	—	Won't fit
Q3_K_M	426.3 GB	427.1 GB	—	Won't fit
Q4_K_M	603.8 GB	604.5 GB	—	Won't fit
Q5_K_M	708.8 GB	709.5 GB	—	Won't fit
Q6_K	820.0 GB	820.8 GB	—	Won't fit
Q8_0	1062.5 GB	1063.3 GB	—	Won't fit
F16	2000.0 GB	2000.8 GB	—	Won't fit

Want the memory numbers alone, at every quantization level and your own context length? Use the Kimi K2.5 VRAM calculator.

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How to Run Kimi K2.5

Install Ollama, then run:

ollama run hf.co/moonshotai/Kimi-K2.5-Instruct-Q4_K_M

Weights on Hugging Face: moonshotai/Kimi-K2.5-Instruct.

Published Benchmark Scores

Quality scores as published by the model's authors or an independent evaluator — not throughput, and not measured by us.

Benchmark	Score	Provenance
SWE-bench Verified	76.8 / 100 %	reported · https://artificialanalysis.ai/articles/kimi-k2-5-everything-you-need-to-know

Best for: coding, agentic, tool use, multimodal, debugging.

Other Kimi K2.5 / K2.6 / K2.7 Sizes

Kimi K2.6 — about 605 GB VRAM at Q4_K_M
Kimi K2.5 1T (32B Active) — about 605 GB VRAM at Q4_K_M
Kimi K2.7 Code — about 605 GB VRAM at Q4_K_M

Kimi K2.5 — Frequently Asked Questions

How much VRAM does Kimi K2.5 need?

About 605 GB at Q4_K_M — quantized weights plus framework overhead, before any KV cache. The cache grows with context length and is added on top; the table above folds it in. Apple Silicon counts unified memory toward the same figure.

Does Kimi K2.5 run on an RTX 4090 (24 GB)?

No. Kimi K2.5 needs about 605 GB at Q4_K_M, more than a single RTX 4090's 24 GB. It needs a larger card, several GPUs, or Apple Silicon with enough unified memory — or it runs with part of the weights offloaded to system RAM, which is much slower.

How do I run Kimi K2.5 locally?

Install Ollama and run `ollama run hf.co/moonshotai/Kimi-K2.5-Instruct-Q4_K_M`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.

What other sizes does Kimi K2.5 / K2.6 / K2.7 come in?

Kimi K2.5 (605 GB), Kimi K2.6 (605 GB), Kimi K2.5 1T (32B Active) (605 GB), Kimi K2.7 Code (605 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.

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