Kimi K2.5 is a 1-trillion-parameter MoE model with 32B active parameters per token. Running it locally requires serious hardware, but the MIT license means you can deploy it however you want.

Update (April 21, 2026): Kimi K2.6 uses the same architecture as K2.5, so this setup guide works for both. K2.6 adds native INT4 QAT quantization for faster inference. See our K2.6 local setup guide for K2.6-specific instructions.

Hardware requirements

Precision	Memory needed	Hardware example
FP16	~2TB	8x H100 cluster
INT8	~1TB	4x H100 or 8x A100
4-bit	~250-300GB	4x A100 80GB

The MoE architecture helps — only 32B parameters activate per token, so inference is faster than a 1T dense model. But you still need enough memory to hold all 1T parameters.

Option 1: vLLM on GPU server

pip install vllm
huggingface-cli download moonshotai/Kimi-K2.5 --local-dir ./kimi-k2.5

python -m vllm.entrypoints.openai.api_server \
  --model ./kimi-k2.5 \
  --tensor-parallel-size 4 \
  --max-model-len 32768 \
  --port 8000

Then connect your tools:

# With Aider
aider --model openai/kimi-k2.5 --openai-api-base http://localhost:8000/v1

# With Kimi CLI
export KIMI_API_BASE="http://localhost:8000/v1"
kimi

Option 2: Cloud GPU rental

Provider	Setup	Cost/hour
Lambda Labs	8x A100	~$10/hr
RunPod	4x A100	~$6/hr
Vast.ai	4x A100	~$4/hr

For occasional use, renting is cheaper than buying. For daily use, the Kimi API at $0.60/1M tokens is more economical.

Option 3: Use smaller alternatives locally

If you don’t have enterprise hardware, these run on consumer GPUs and offer good quality:

Model	VRAM needed	Quality vs K2.5
Qwen 3.5 27B	16GB	~75%
Gemma 4 27B	16GB	~75%
Devstral Small 24B	14GB	~70%
Codestral 22B	12GB	~65% (autocomplete)

The practical recommendation: use K2.5 via API ($0.60/1M) for complex tasks and a local 27B model for routine work. See our cheapest AI coding setup guide.

Related: Kimi K2.5 Complete Guide · Best GPU for AI Locally · Best AI Models for Mac · How To Setup Open Webui