LLM Training Scalability: Parallelism Strategies for Transformers

This article, Chapter 4 of a series on scaling Large Language Models (LLMs), delves into the complexities of efficiently training transformer models across multiple accelerators. It highlights the challenge of maintaining performance and throughput as the number of accelerators increases, due to the overhead of data transfer and synchronization. The chapter will explore four types of parallelism, their respective advantages and disadvantages, and how they can be combined within a single computational cluster. AI

IMPACT Explains methods for efficient LLM training across multiple accelerators, crucial for developing larger and more capable models.

RANK_REASON Article discusses technical details of training LLMs, fitting research. [lever_c_demoted from research: ic=1 ai=1.0]

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• Chapter 4
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• Scaling LLMs: From a Single Chip to the Data Center
• Transformer++

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