LLM
How to deploy Kimi-K2-Instruct on a GPU cloud
A 1026B (MoE) language model for chat and instruction-following. Full specs, license and use cases.
Kimi-K2-Instruct size and hardware requirements
1026.4B
Total parameters
Mixture-of-experts: 8 of 384 experts active per token (exact active-parameter count not stated on the model card)
Active parameters
F8_E4M3
Published precision
1147.1 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP8 (native) | 955.9 GB | 1147.1 GB | No capable live offer found | – | – |
| INT4 (quantized) | 478.0 GB | 573.6 GB | A100 | 8 | $6.80/hr |
How to run Kimi-K2-Instruct
Run Kimi-K2-Instruct with vLLM
From moonshotai/Kimi-K2-Instruct's own deployment docs.
# start ray on node 0 and node 1
# node 0:
vllm serve $MODEL_PATH \
--port 8000 \
--served-model-name kimi-k2 \
--trust-remote-code \
--tensor-parallel-size 16 \
--enable-auto-tool-choice \
--tool-call-parser kimi_k2Source: https://huggingface.co/moonshotai/Kimi-K2-Instruct/raw/main/docs/deploy_guidance.md
Run Kimi-K2-Instruct with GGUF quantizations
Prebuilt GGUF weights published at unsloth/Kimi-K2-Instruct-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf unsloth/Kimi-K2-Instruct-GGUFSource: https://huggingface.co/unsloth/Kimi-K2-Instruct-GGUF
Deploy Kimi-K2-Instruct on Aquanode
Aquanode has no one-click deploy template for Kimi-K2-Instruct; you install the inference engine yourself with the commands below. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch a bare GPU pod sized to the requirement above (1147 GB VRAM or more).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch a GPU pod