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Mastering AI Compute: From GPU Kernels to Inference Economics
Paperback
Publisher: Independently Published
Published: Oct 2 2026
Pages: 166
Weight: 0.66
Height: 0.35 Width: 7.00 Depth: 10.00
Language: English
A faster GPU does not automatically produce a faster training job or a cheaper inference service. The result depends on the whole path: workload shape, memory traffic, communication, recovery, scheduling and the definition of useful output.
Mastering AI Compute teaches AI infrastructure engineers and experienced systems engineers how to reason about that path. Start with transformer shapes, a training step and the generation loop. Build workload budgets, read GPU profiles, map parallelism and communication, account for checkpoint durability, and diagnose admission, placement and data stalls. Then follow serving from prefill and decode through KV-cache allocation, batching, quantization and speculative decoding to latency, quality and cost per useful output.
The book works through named systems including FlashAttention-3, NCCL, FSDP2, Megatron, Kubernetes, Kueue, vLLM and MLPerf. Published GPU profiles and logs are embedded as annotated tables and original data figures. CPU worksheets and a tested small training/restart example accompany the chapters. Evidence labels distinguish published results, calculations and book-authored measurements. GPU and cluster experiments are recipes for the reader to run on their own hardware.
For engineers who design, operate and pay for AI compute, this is a systems reference for asking better questions, reading the evidence and choosing the next test.
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