The excerpts provide an extensive guide on scaling Large Language Model (LLM) training across GPU clusters, detailing five core parallelism strategies: Data Parallelism (DP), Tensor Parallelism (TP), Sequence/Context Parallelism (SP/CP), Pipeline Parallelism (PP), and Expert Parallelism (EP). The text first addresses memory optimization techniques like activation recomputation and gradient accumulation before exploring how to distribute the model and data using methods like the ZeRO optimizer and various pipeline schedules to minimize idle GPU time. Finally, the source transitions to hardware-level optimizations, covering GPU architecture, the implementation of custom kernels (e.g., in Triton and CUDA), techniques like memory coalescing and tiling, and the use of mixed precision training to maximize throughput and computational efficiency. The discussion emphasizes the critical trade-off between memory savings, computation time, and communication overhead when configuring large-scale training