"Amazon Web Services has announced a significant breakthrough in container orchestration with Amazon Elastic Kubernetes Service (EKS) now supporting clusters with up to 100,000 nodes, a 10x increase from previous limits. This enhancement enables unprecedented scale for artificial intelligence and machine learning workloads, potentially supporting up to 1.6 million AWS Trainium chips or 800,000 NVIDIA GPUs in a single Kubernetes cluster."
"The most advanced AI models, with trillions of parameters, demonstrate significantly superior capabilities in context understanding, reasoning, and solving complex tasks. Running them within a single cluster offers certain key benefits. First, it lowers compute costs by driving up utilization through a shared capacity pool for running heterogeneous jobs ranging from large pre-training to fine-tuning experiments and batch inferencing. Additionally, centralized operations such as scheduling, discovery, and repair are significantly simplified compared to managing split-cluster deployments."
Amazon EKS supports Kubernetes clusters up to 100,000 nodes, a tenfold increase that enables single-cluster AI/ML workloads at extreme scale. Such clusters can potentially host up to 1.6 million AWS Trainium chips or 800,000 NVIDIA GPUs, enabling training of trillion-parameter models without cross-cluster partitioning. Single-cluster deployments improve utilization, reduce compute costs by pooling heterogeneous jobs, and simplify centralized operations like scheduling, discovery, and repair. AWS achieved this scale through architectural re-engineering of Kubernetes while maintaining conformance. The core etcd store was overhauled by offloading consensus from a raft-based implementation to a journal system that delivers ultra-fast, ordered multi‑AZ replication.
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