Deploying Kimi K3 on Amazon SageMaker HyperPod and Amazon EKS | Amazon Web Services
Open weight models have become powerful enough to handle complex tasks such as multi-step agentic workflows, advanced reasoning, and long-horizon coding. However, as these models grow in capability, they also grow in size and hosting multi-trillion parameter architectures requires purpose-built infrastructure, high-end GPU compute, and optimized serving frameworks. On July 27, 2026, Moonshot AI released Kimi K3, a 2.8 trillion parameter Mixture of Experts (MoE) model that represents the first open-weight system to reach the 3 trillion parameter class. Kimi K3 delivers frontier-level intelligence while making its weights publicly available, so that organizations can self-host one of the most capable models in existence on their own infrastructure.
This post walks through deploying Kimi K3 on AWS using two approaches: Amazon SageMaker HyperPod, and Amazon Elastic Kubernetes Service (Amazon EKS) cluster.
About Kimi K3
Kimi K3 is built on a differentiated architecture featuring Kimi Delta Attention (KDA), Gated Multi Head Latent...
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