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 Full video on youtube, tiktok, substack, etc All my links: https://linktr.ee/learnbydoingwithsteven #steven数据漫谈 #大型语言模型 #AI工程化 #深度学习 #分布式计算 #推理优化 #技术综述 #LLM #AI #DeepLearning #DistributedComputing #InferenceOptimization #TechnicalReview