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Google Cloud Networking Supports Fluid Compute Choices for AI Workloads

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announcement

This article details how Google Cloud networking infrastructure supports flexible AI workload deployments across various compute environments. It explores strategies for managing accelerator resource availability, including dynamic scheduling and reservations, to ensure optimal performance and cost. The discussion covers specific networking configurations for different GPU and TPU types, highlighting their unique architectures. This guide is relevant for architects and engineers designing scalable and resilient AI solutions on Google Cloud.

  • →Flexible Capacity Management for AI Accelerators
  • →Networking Configurations for GPU Accelerators
  • →Networking Architectures for TPU Accelerators
  • →Cloud Run Integration for Serverless AI Inference
Notes (4) ›
  • Flexible Capacity Management for AI Accelerators

    Google Cloud addresses AI accelerator resource availability challenges by offering fluid compute options. These include Dynamic Workload Scheduler (Flex-start and calendar mode), Future and Flex reservations, GKE's dynamic node auto-provisioning with ComputeClasses, and Spot VMs for cost optimization.

  • Networking Configurations for GPU Accelerators

    The platform supports various GPU networking setups: standard gVNIC over TCP/IP for general workloads; accelerated GPUDirect-TCPX/TCPXO fabrics for NVIDIA H100; and RoCEv2 fabrics for NVIDIA H200, B200, and GB200/GB300, leveraging dedicated RDMA VPCs and GKE DRANET for automated setup.

  • Networking Architectures for TPU Accelerators

    Cloud TPUs utilize ultra-low-latency inter-chip interconnects (ICI) in 2D/3D meshes and optical circuit switches (OCS) for dynamic reconfigurations. Newer generations like TPU v6e and TPU7x feature multi-NIC architectures to isolate management and data traffic, with GKE DRANET supporting automated provisioning for TPU communication.

  • Cloud Run Integration for Serverless AI Inference

    Cloud Run GPU services support NVIDIA L4 and RTX PRO 6000 accelerators, enabling serverless AI inference. Direct VPC Egress allows these containers to securely and with low-latency access internal data sources without public internet traversal.

Read the original announcement →

https://cloud.google.com/blog/topics/developers-practitioners/how-google-cloud-networking-supports-your-fluid-compute-choices-for-ai-workloads/

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