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Academic
Bare-Metal AI: Exploiting Linux Internals for Extreme Data Science
Most AI engineers are leaving massive performance on the table while their competitors struggle with Python abstractions and default configurations—but "Bare-Metal AI" reveals the underground kernel-level techniques that can reduce training time by 80% and slash inference latency to microseconds. This isn't theory: you'll master production-ready eBPF observability that exposes GPU starvation, io_uring zero-copy architectures that move terabytes without CPU overhead, RDMA clusters achieving 5x faster distributed training, NUMA-aware scheduling for 70B+ parameter models, and real-time kernel tuning for high-frequency inference. Every technique includes implementation checklists, diagnostic decision trees, and real case studies from scaling 128-node LLM factories, plus an arsenal of custom memory allocators, kernel probes that hunt down "impossible" memory leaks, and Cgroups v2 isolation strategies that prevent noisy neighbor problems.
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Most AI engineers are leaving massive performance on the table while their competitors struggle with Python abstractions and default configurations—but “Bare-Metal AI” reveals the underground kernel-level techniques that can reduce training time by 80% and slash inference latency to microseconds. This isn’t theory: you’ll master production-ready eBPF observability that exposes GPU starvation, io_uring zero-copy architectures that move terabytes without CPU overhead, RDMA clusters achieving 5x faster distributed training, NUMA-aware scheduling for 70B+ parameter models, and real-time kernel tuning for high-frequency inference. Every technique includes implementation checklists, diagnostic decision trees, and real case studies from scaling 128-node LLM factories, plus an arsenal of custom memory allocators, kernel probes that hunt down “impossible” memory leaks, and Cgroups v2 isolation strategies that prevent noisy neighbor problems.
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