Deploying Kimi K3 on AWS
Quick Answer
Moonshot AI's Kimi K3, a 2.8 trillion parameter Mixture of Experts model, enables advanced AI capabilities on AWS, requiring high-end GPU infrastructure.
Quick Take
It supports long-horizon coding and complex reasoning, with weights available for self-hosting on Hugging Face.
Key Points
- Kimi K3 features 2.8 trillion parameters, activating 104 billion per token.
- Requires p6-b300 instances with 8 NVIDIA B300 GPUs for deployment.
- Available on Hugging Face under moonshotai/Kimi-K3 in MXFP4 format.
- Supports native multimodal processing for text and vision tasks.
- Deployable via Amazon SageMaker HyperPod or Amazon EKS.
DeepSignal Analysis
What happened
Moonshot AI launched Kimi K3, a 2.8 trillion parameter Mixture of Experts model, on July 27, 2026. This model is designed for complex tasks and is available for self-hosting on Hugging Face. Deployment on AWS can be achieved via Amazon SageMaker HyperPod or Amazon EKS.
Key evidence
- Kimi K3 features 2.8 trillion parameters distributed across 896 experts, activating only 16 per token, which improves scaling efficiency by 2.5 times compared to Kimi K2.
- The model supports a context window of 1 million tokens and is capable of handling multimodal inputs, including text and vision.
- To deploy Kimi K3, AWS requires a p6-b300 instance, which includes 8 NVIDIA B300 Blackwell Ultra GPUs, ensuring efficient tensor-parallel inference.
Why it matters
The release of Kimi K3 marks a significant advancement in AI model capabilities, particularly for organizations needing to perform complex reasoning and long-horizon coding. Its open-weight availability allows for broader access and experimentation in AI applications. The infrastructure requirements highlight the increasing demand for high-performance computing resources in AI development.
What to watch
Source Excerpt
This post walks through deploying Kimi K3 on AWS using two approaches: Amazon SageMaker HyperPod, and Amazon Elastic Kubernetes Service (Amazon EKS) cluster.
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