The content explores the complexities of developing large language models in data centers, focusing on challenges like infrastructure failures, framework errors, and script issues. It analyzes resource utilization patterns, job durations, queuing delays, and provides insights into failure categories and their impact on GPU resources.
The study reveals discrepancies between LLM workloads and prior DL workloads in terms of job duration and resource utilization. It discusses the imbalance in resource usage, high GPU idle time during evaluation workloads, and frequent job failures affecting training efficiency. The analysis also includes a detailed breakdown of failure categories such as infrastructure issues, framework errors, and script failures.
Furthermore, the content examines the environmental impact of LLM development in terms of energy consumption and carbon emissions. It offers insights into workload profiling for pretraining and evaluation tasks, showcasing challenges like high model loading overhead and metric computation delays. Additionally, it provides a comprehensive failure analysis highlighting common failure types and their implications on GPU resources.
Overall, the study sheds light on the intricate process of developing large language models in data centers, addressing key challenges faced by researchers and engineers in optimizing system performance.
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