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AI Workload Optimization with GPUs, CUDA, and PyTorch: A Practical Guide to Faster Training, Lower Inference Latency, Better Throughput, and Scalable
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Publisher: Independently Published
Pages: 212
Weight: 0.83
Height: 0.45 Width: 7.00 Depth: 10.00
Language: English
Your AI model may work-but is it fast enough, efficient enough, and stable enough to survive real training and production use?
Slow training jobs, idle GPUs, memory crashes, weak throughput, high inference latency, and expensive cloud runs can turn a promising AI project into a costly engineering problem. Adding more hardware is not always the answer. If you do not know where the bottleneck is, you may waste time tuning the wrong part of the system.
AI Workload Optimization with GPUs, CUDA, and PyTorch gives you a practical, measurement-first workflow for improving AI performance without guesswork. Built around the baseline, profile, optimize, verify method, this book helps you identify what is slowing down your workload, apply the right optimization, and confirm the result with clear metrics.
Inside, you will learn how to:
- Benchmark training and inference correctly
- Profile PyTorch workloads before changing code
- Improve GPU utilization, memory use, and data loading
- Apply mixed precision, torch.compile, and CUDA-aware optimization carefully
- Scale training across multiple GPUs
- Optimize inference with PyTorch, ONNX Runtime, TensorRT, Triton, and vLLM
- Measure latency, throughput, tail latency, tokens per second, and cost
This book is written for machine learning engineers, software engineers, data scientists, AI infrastructure builders, and students who want practical GPU performance skills. The examples are self-contained, with code, commands, scripts, and project materials included directly in the book-no external companion repository required.
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