I Pushed GPU Utilization From 18% to 90%+

Are you paying for expensive GPU compute while your GPU spends part of its time waiting? Low GPU utilization doesn't necessarily mean you need a faster GPU. The real bottleneck may be your data pipeline, batch size, CPU processing, synchronization, memory usage, kernel execution, or inference workload. In this video, we look at WHY GPUs sit idle and HOW to systematically improve GPU utilization for AI and machine-learning workloads. We cover: • Why low GPU utilization happens • How to profile CPU and GPU activity before optimizing • DataLoader workers, prefetching and pinned memory • Dynamic batching for inference workloads • A practical Profile for ideal compute workflow Chapters: 00:00 Your Expensive GPU Is Waiting 00:38 What Low GPU Utilization Really Means 01:20 Why GPUs Sit Idle 02:02 Profile Before You Optimize 02:48 Fix the Data Pipeline 03:42 Batches & Micro-batches 04:30 Mixed Precision: FP16 and BF16 05:17 When memory not compute is limiter 06:05 Reduce Kernel Overhead 06:50 When One GPU Isn't Enough 07:45 Optimization loop 08:34 Summary If you work with AI Engineering, MLOps, GPU infrastructure, Kubernetes, model training or production inference, this video will help you think about GPU performance as a complete system rather than just a hardware problem. Subscribe to RajeshScribe for practical videos on: Cloud • AI • MLOps • AI Infrastructure • Production AI Engineering Blog & Portfolio: RajeshScribe.com #GPUOptimization #MLOps #AIEngineering