Deploying Kimi K3 on AWS: Scalable AI Model Hosting

The recent release of Kimi K3 by Moonshot AI marks a major milestone in open-weight AI models, featuring 2.8 trillion parameters distributed across 896 experts, with only 16 active per token. This architecture enhances scaling efficiency and supports complex reasoning, long-horizon coding, and agentic workflows. The model's weights are publicly available on Hugging Face in MXFP4 format, optimized for large-scale inference.

Deploying Kimi K3 requires substantial GPU compute, specifically a p6-b300 instance with 8 NVIDIA B300 Blackwell Ultra GPUs, which are essential for tensor-parallel inference. AWS offers two deployment pathways: using Amazon SageMaker HyperPod with the Inference Operator for simplified, managed deployment, or setting up a dedicated EKS cluster for more control. Both methods involve provisioning GPU capacity via reserved capacity plans or Capacity Blocks, ensuring reliable access to high-performance hardware.

The HyperPod approach automates container orchestration, model loading, and endpoint management, exposing an OpenAI-compatible API for easy integration. Alternatively, deploying on EKS involves provisioning a GPU-optimized cluster with Terraform, installing GPU drivers, deploying the inference server with Helm, and exposing the endpoint via Kubernetes services. Once operational, the model can be invoked with standard API calls, supporting advanced features like structured output and multi-step reasoning.

This deployment capability allows organizations to leverage Kimi K3's advanced reasoning and multimodal abilities in their own infrastructure, fostering innovation in AI applications. The use of AWS's scalable infrastructure ensures that even the largest models can be hosted efficiently and securely, supporting research, development, and enterprise deployment of cutting-edge AI systems.