SURGE system optimizes GPU encoding for large-scale text embedding generation

Researchers have developed SURGE, a new system designed to improve the efficiency of generating text embeddings on GPUs. SURGE addresses the bottleneck of processing numerous small data partitions by employing a streaming SuperBatch approach, which significantly reduces memory usage and speeds up the time to first output compared to traditional fixed-batch methods. The system has been deployed in production, handling over 800 million texts and demonstrating a 68x faster time-to-first-output with substantially lower memory requirements. AI

影响 This system could improve the efficiency and scalability of embedding generation pipelines for large-scale AI applications.

排序理由 The cluster contains an arXiv preprint detailing a new system for GPU encoding. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

• GPU
• NVIDIA L4

• 论文
• 基础设施

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