1. GKE supports large-scale AI workloads with Cloud TPU v5p and A3 Mega powered by NVIDIA’s H100 GPUs.
2. GKE improves cost efficiency for AI workloads with container and model preloading, GPU sharing, and GCS FUSE read caching.
3. GKE enhances ease of use for AI workloads with features like Dynamic Workload Scheduler, GKE Autopilot, and automatic GPU driver installation.
Google Kubernetes Engine (GKE) is a popular choice for customers with AI workloads due to its open, portable, cloud-native, and customizable platform. The use of GPUs and TPUs on GKE has grown significantly over the past year, demonstrating the increasing adoption of AI technologies.
GKE has built innovations focused on scale, cost efficiency, and ease of use for customers transforming their businesses with AI. Large-scale AI workloads are supported through accelerators like Cloud TPU v5p and A3 Mega, enabling faster training of large models. Multi-slice training on GKE allows for cost-effective, large-scale training with near-linear scaling.
To enhance cost efficiency, GKE now supports container and model preloading, reducing cold start times and improving GPU utilization. GPU sharing with NVIDIA Multi-Process Service and GCS FUSE read caching further optimize AI accelerator usage during model training.
GKE’s ease of use is highlighted through features like Dynamic Workload Scheduler and GKE Autopilot, which now supports NVIDIA H100 GPUs and TPUs. The platform also embeds AI into cloud operations through Gemini Cloud Assist, offering features for optimizing costs, troubleshooting, and synthetic monitoring.
Google Cloud continues to invest in foundational areas for GKE, introducing new capabilities like GKE threat detection for container runtime attacks and GKE compliance for automatic compliance scanning against benchmarks. These enhancements ensure the stability, security, and compliance of cloud-native applications for modern enterprises.