PyTorch Fundamentals: Mastering Tensor Efficiency and Large-Scale Model Training

This comprehensive guide covers essential PyTorch concepts critical for efficient LLM training, from memory-conscious floating-point precision choices (FP32, BF16, FP8) to computing FLOPs for performance benchmarking. Understanding tensor operations, custom optimizers, and practical infrastructure decisions—like memory-mapped data loading and checkpointing—directly impacts training cost and scalability, making this knowledge crucial for anyone building production-grade deep learning systems.