The LLM Lifecycle: From Distributed Pre-training to High-Efficiency Inference

 The LLM Lifecycle: From Distributed Pre-training to High-Efficiency Inference
The evolution of Large Language Models (LLMs) has shifted from a mere parameter race to a sophisticated systems engineering challenge. A new comprehensive review analyzes the complete LLM lifecycle.
The report identifies the Transformer architecture and its variants, particularly Causal Decoders, as the enduring foundation of modern LLMs. During the pre-training phase, frameworks like Distributed Data Parallel (DDP), Pipeline Parallelism, and ZeRO have become essential for managing billion-parameter scale training. However, the next frontier lies in inference optimization. Techniques such as Knowledge Distillation, Quantization, and Low-Rank Approximation are now pivotal for reducing VRAM footprints and latency without sacrificing intelligence.
Furthermore, refined mixed-precision training and checkpointing mechanisms are enabling developers to achieve superior model performance within constrained compute budgets. For AI engineers, the future core competency lies in mastering end-to-end systems engineering, not just model fine-tuning.
https://arxiv.org/abs/2401.02038
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