Deploying Kimi K3 on Amazon SageMaker HyperPod and Amazon EKS

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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 Attention (MLA), and a Stable LatentMoE framework. The model distributes its 2.8 trillion parameters across 896 specialist experts, activating only 16 per token. This means approximately 104 billion parameters are active during any single forward pass, yielding a 2.5x improvement in scaling efficiency over its predecessor, Kimi K2. Attribute Value Total Parameters 2.8 Trillion Active Parameters per Token 104 Billion Architecture Mixture of Experts (MoE) Expert Count 896 (16 activated per token) Context …

Vollständiger Originalartikel ↗

• Betroffenheit im eigenen Stack prüfen: Versionen/Komponenten abgleichen.
• Originalquelle / Hersteller-Advisory lesen: https://aws.amazon.com/blogs/machine-learning/deploying-kimi-k3-on-amazon-sagemaker-hyperpod-and-amazon-eks/
• Verfügbaren Patch oder Workaround einspielen und dokumentieren.