The New Stack Podcast How Oracle Is Meeting the Infrastructure Needs of AI
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Jan 23, 2025 Sudha Raghavan, SVP for Developer Platform at Oracle Cloud Infrastructure, discusses the seismic shift in infrastructure needs driven by AI's rapid adoption. She highlights the explosive demand for GPUs and the challenges posed by continuous workloads like large language model training. Raghavan elaborates on Oracle's innovative GPU superclusters and improvements in Kubernetes for better job management and observability. The conversation also touches on the evolution of AI pipelines and the critical trade-offs in AI model selection to meet specific business demands.
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GPU Workloads Demand Peak Power
- GPUs are constantly running at peak power during AI workloads, unlike CPU web workloads which have peaks and troughs.
- This leads to higher power consumption and hardware failure rates, requiring new infrastructure solutions like large GPU superclusters.
Checkpoint and Kubernetes for GPUs
- Implement checkpointing carefully to save progress frequently in stateful AI jobs and enable resumption after failure.
- Enhance Kubernetes with GPU-specific features like node placement and workload scheduling for efficient, resilient AI operations.
Abstracting GPU Metrics Uniformly
- Different GPU chipmakers present varied metrics which complicate monitoring.
- Oracle's Node Manager abstracts hardware differences into a uniform API for Kubernetes to simplify cluster management.