Kimi K2.6 — VRAM, speed & local setup
Written by Jakub Rusinowski · Last updated
April 2026 successor to K2.5, same 1T-parameter MoE shape: 384 experts with 8 routed plus 1 shared, 61 layers, MLA attention, 256K context, Modified MIT. Adds video alongside image input through MoonViT. 80.2% on SWE-bench Verified and 66.7% on Terminal-Bench 2.0 — the strongest open-weight coder at release. Like K2.5 it needs ~605 GB resident at Q4_K_M: the 32B figure is active parameters per token, not what fits on your card.
Kimi K2.6 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.
VRAM and speed by quantization
Quoted against NVIDIA RTX 4090 (24 GB). Weights plus overhead only: this model's architecture is unpublished, so no KV cache is added.
| Quant | Memory | VRAM | Speed (est.) | Fit |
|---|---|---|---|---|
| Q2_K 2.63 bpw | 329.6 GB | — | Too big | |
| Q3_K_M 3.41 bpw | 427.1 GB | — | Too big | |
| Q4_K_M 4.83 bpw | 604.6 GB | — | Too big | |
| Q5_K_M 5.67 bpw | 709.6 GB | — | Too big | |
| Q6_K 6.56 bpw | 820.8 GB | — | Too big | |
| Q8_0 8.50 bpw | 1063.3 GB | — | Too big | |
| F16 16.00 bpw | 2000.8 GB | — | Too big |
Black marker = usable memory on the NVIDIA RTX 4090 (24 GB). Estimates from the memory-bandwidth roofline on the methodology page. Kimi K2.6 VRAM calculator →
Get Kimi K2.6 running
Kimi K2.6 is not practical on a single consumer GPU: it needs about 604.6 GB at Q4_K_M, more than any single device in our catalogue holds with headroom. How multi-GPU inference works, or rent a multi-GPU server by the hour:
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How to run Kimi K2.6
Install Ollama, then run:
ollama run hf.co/moonshotai/Kimi-K2.6-Instruct-Q4_K_MSpecifications
Verified — Checked against the primary source — the model card or the vendor spec page — and corroborated by a second independent source.
- Parameters
- 1 Trillion (32B active)
- Context window
- 256,000
- Architecture
- MoE (384 experts, 8+1 shared, MLA) + MoonViT vision
- Provider
- Moonshot AI
- Licence
- Modified MIT
- Practical deployment
- Private cloud / datacenter cluster
- Specified at
- Q4_K_M
- System RAM
- 768 GB
- Record updated
- 2026-09-06
Commercial use permitted. No usage restrictions beyond attribution.
Quality and use cases
Scores as published by the model’s authors or an independent evaluator — quality, not throughput, and not measured by us.
| Benchmark | Score | Provenance |
|---|---|---|
| SWE-bench Verified | 80.2 / 100 % | reported · https://benchlm.ai/models/kimi-2-6 |
| Terminal-Bench 2.0 | 66.7 / 100 % | reported · https://benchlm.ai/models/kimi-2-6 |
Other Kimi K2.5 / K2.6 / K2.7 sizes
Kimi K2.6 — frequently asked questions
How much VRAM does Kimi K2.6 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 K2.6 run on an RTX 4090 (24 GB)?
No. Kimi K2.6 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 K2.6 locally?
Install Ollama and run `ollama run hf.co/moonshotai/Kimi-K2.6-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.