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65,000 nodes and counting: Google Kubernetes Engine is ready for trillion-parameter AI models
- Google Kubernetes Engine now supports up to 65,000-node clusters, providing an unmatched scale for training or inference with much-needed capacity for the world’s most resource-hungry AI workloads.
- This advancement enables customers to reduce model training time or scale models to multi-trillion parameters or more.
- GKE’s capacity offers more than 10X larger scale than the other two largest public cloud providers, according to Google.
- GKE is transitioning from the open-source etcd, distributed key-value store, to a new, more robust, key-value store based on Spanner, Google’s distributed database that delivers virtually unlimited scale.
- On top of supporting larger GKE clusters, this change will provide improved latency of cluster operations and a stateless cluster control plane.
- GKE now scales significantly faster with a control plane that automatically adjusts to high-volume operations, while maintaining predictable operational latencies.
- Guided by Google's long-standing and robust open-source culture, GKE provides substantial contributions to the open-source community, including when it comes to scaling Kubernetes.
- Recent innovations in this space include Fully managed DCGM metrics for improved accelerator monitoring and Custom compute classes, which offer greater control over compute resource allocation and scaling.
- GKE’s support for larger clusters will help companies like Anthropic accelerate AI innovation and work more efficiently across diverse workloads.
- At Google Cloud, the company is dedicated to providing the best platform for running containerized workloads, consistently pushing the boundaries of innovation.
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