Explore the full engineering blog here: https://www.linkedin.com/pulse/beyond-gpu-power-compute-roi-flywheel-jitendra-agarwal-a2ezc/

This blog post by a Netflix ML platform lead discusses maximizing the return on investment (ROI) of GPUs used in AI. It explains the crucial role of GPUs in AI workflows, including model training, fine-tuning, and inference, while highlighting the challenges of managing GPU resources effectively. The author introduces a "Compute ROI Flywheel" framework with five stages—investment, optimization, performance tuning, developer productivity, and minimizing idle resources—to improve GPU utilization. Practical tips are provided to optimize GPU compute across the AI lifecycle, emphasizing the importance of benchmarking, tracking utilization, right-sizing jobs, tuning workflows, and planning for capacity. Ultimately, the post advocates for a holistic approach to GPU management to achieve substantial ROI and enhance AI capabilities.