Orchestrating ML/AI workloads with TPUs on GKE

Key Concepts

• TPU (Tensor Processing Unit): Google’s custom ASIC designed for high-speed matrix multiplication in AI/ML workloads.
• GKE (Google Kubernetes Engine): The managed environment that orchestrates TPU resources, abstracting hardware complexity.
• MXU (Matrix Multiplication Unit): The core hardware component in TPUs that performs massive matrix math in a single step.
• HBM (High-Bandwidth Memory): On-chip memory that reduces data transfer bottlenecks for large models.
• Goodput: A metric representing the actual amount of useful training work completed, accounting for infrastructure failures and recovery.
• Atomic Provisioning: Treating TPU slices (single host, multi-host, or multi-slice) as a single unit for scheduling, scaling, and failover.
• DWS (Dynamic Workload Scheduler): A scheduling system offering "Flex" (pay-as-you-go) and "Calendar" (reserved) modes.
• JobSet: An orchestration framework for managing multi-host/multi-slice training jobs as a single atomic unit.
• Kueue: A job queuing system that manages resource quotas and gang scheduling.

1. TPU Architecture and Scaling

TPUs are specialized ASICs optimized for the heavy matrix calculations required by LLMs and recommendation models.

• Hardware Evolution: The 7th generation Ironwood TPU supports up to 9,216 chips in a single pod, offering massive jumps in TFLOPS (for BF16/FP8) and HBM bandwidth compared to previous generations like Trillium and v4.
• Interconnects: TPUs utilize high-speed Inter-Chip Interconnect (ICI) links and optical circuit switching to scale from a single chip to thousands, enabling massive distributed training.

2. GKE Infrastructure for TPUs

GKE acts as the orchestration layer that makes TPU clusters manageable.

• Atomic Slices: GKE organizes TPUs into "slices" (single-host, multi-host, or multi-slice). A multi-slice configuration connects multiple GKE node pools over the data center network, allowing for clusters of 50k–100k+ chips.
• Storage Integration: To prevent I/O bottlenecks, GKE supports:
  • GCS Fuse: Bridges object storage with file systems, providing up to 9x faster model loading via caching.
  • Managed Lustre: A parallel file system for high-concurrency I/O.
  • Hyperdisk ML: Delivers up to 1.2 TB/s aggregate throughput to hydrate model weights 12x faster than standard disks.

3. Scheduling and Obtainability

GKE provides flexible ways to acquire TPU capacity:

• DWS Flex: A pay-as-you-go model for bursty, experimental, or fine-tuning workloads, providing up to 7 days of uninterrupted compute.
• Calendar Mode: A dedicated reservation system (1–3 months) for critical, long-running training jobs that require guaranteed uptime.
• Custom Compute Classes: Allows users to define a prioritized hierarchy of obtainability. If a primary resource (e.g., a reserved Trillium chip) is unavailable, GKE automatically falls back to secondary options (e.g., Spot or On-Demand).

4. Reliability and "Goodput" Optimization

GKE focuses on maximizing "goodput" through automated resilience:

• Smart Repair: If a node in a large TPU slice fails, GKE automatically recreates the entire atomic slice to ensure ICI synchronization, rather than just rebooting a single VM.
• Multi-tier Checkpointing: To eliminate "checkpointing tax," GKE writes checkpoints to local RAM first, then to adjacent nodes, and finally to cloud storage. This keeps MXUs active and improves training goodput by ~6% on large-scale runs.
• Hot Swapping: If a node becomes unhealthy, GKE can preempt lower-priority workloads to move high-priority jobs to healthy silicon, minimizing downtime.

5. Inference Optimization

• GKE Inference Gateway: Unlike traditional round-robin load balancing, this gateway is "model-aware." It monitors KV cache utilization and local queues of VLLM-based serving models to route requests to the least-loaded VM, reducing latency by up to 60% and improving cost efficiency by 30%.

6. Developer Experience

To lower the barrier to entry for ML engineers who may not be Kubernetes experts, Google provides:

• XPK (Accelerated Processing Kit): A command-line tool that masks Kubernetes complexity, allowing users to deploy workloads with simple commands.
• Cluster Toolkit: Terraform-based blueprints for enterprise-grade infrastructure deployment.

Synthesis

The integration of TPUs into GKE represents a shift from treating accelerators as raw hardware to treating them as a cohesive, "AI-native" infrastructure. By implementing features like atomic scheduling, multi-tier checkpointing, and model-aware inference gateways, GKE effectively manages the complexities of large-scale AI training. The primary takeaway is that GKE is no longer just a container orchestrator; it is an AI hyper-compute platform designed to maximize "goodput" and simplify the operational burden for ML engineers.