Google Kubernetes Engine gets AI-powered upgrades for faster, safer workloads
Google LLC has unveiled several enhancements to its Google Kubernetes Engine (GKE) at KubeCon + CloudNativeCon North America in Atlanta. The tech giant has introduced the GKE Agent Sandbox and optimized the service for large-scale AI jobs with the GKE Inference Gateway.
The GKE Agent Sandbox, a secure environment for housing large language model (LLM)-generated code and tools, uses gVisor for isolation. It offers sandbox snapshots and container-optimized compute. Meanwhile, the GKE Inference Gateway reduces latency and improves throughput for AI workloads with LLM-aware routing and disaggregated serving.
Google has also improved GKE's autoscaling with parallel node-provisioning operations and a buffer of preprovisioned nodes. Additionally, the company has open-sourced its multi-tier checkpointing (MTC) solution to speed up workload recovery. Furthermore, GKE container image streaming has been revamped to allow applications to start running before the entire container image is downloaded. GKE Pod Snapshots can save up to 80% in start times for LLMs by restoring memory snapshots from Google Cloud Storage.
These updates to Google Kubernetes Engine aim to enhance security, efficiency, and scalability for AI workloads. The GKE Agent Sandbox and GKE Inference Gateway, along with other improvements, demonstrate Google's commitment to advancing Kubernetes for AI applications. The tech giant's efforts align with the growing adoption of AI agents among senior IT leaders, with 79% already using them and 88% planning to increase IT budgets for agentic AI.
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