Generative AI models are only as fast and affordable as the infrastructure running them. This guide from engineer Chris Fregly walks through the full stack of decisions that separate a sluggish, expensive AI system from one that scales efficiently, covering GPU internals, distributed training, and inference serving in equal depth.
What's covered
- Tuning CUDA cores and getting the most out of GPU hardware
- Scaling distributed training and inference across multiple nodes
- Building low-latency, high-throughput inference servers
- Spotting and removing bottlenecks in real AI pipelines
- Applying full-stack optimization for stable, reliable systems
A practical reference
Rather than abstract theory, the material closes with a checklist of more than 175 ready-to-apply optimizations, giving engineers, researchers, and developers concrete techniques to cut costs and boost throughput on both training and inference workloads.
