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Deploying Kimi K3 on AWS | 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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About Kimi K3

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Astronomers Discover the Existence of a Black Hole Star

Turns Out, a New Shape Is Exactly What Samsung Needed for Its Folding Phone

These cheap open earbuds are so good for the money, they had me wondering why you’d spend more on expensive rivals from Shokz and Bose

How to watch Community Shield 2026: FREE live streams and TV channels for Arsenal vs Man City