Optimizing Large Language Models for Efficiency and Effectiveness

The field of large language models (LLMs) is shifting its focus from solely improving model size and capability to optimizing the surrounding ecosystem. This includes enhancing data quality and management, computational efficiency, latency, and evaluation frameworks to ensure modern AI services are efficient and profitable. Researchers are exploring specialized LLM inference engines that integrate optimization methods into service-oriented infrastructures, and investigating techniques such as parallelism, compression, and caching to reduce costs. Additionally, there is a growing emphasis on supporting complex LLM-based services, various hardware, and enhanced security. Noteworthy papers in this area include:

  • A Survey on Inference Engines for Large Language Models, which provides a comprehensive evaluation of open-source and commercial inference engines.
  • HEXGEN-TEXT2SQL, which introduces a novel framework for scheduling and executing agentic multi-stage LLM-based Text-to-SQL workflows on heterogeneous GPU clusters.

Sources

A Survey on Inference Engines for Large Language Models: Perspectives on Optimization and Efficiency

Beyond the model: Key differentiators in large language models and multi-agent services

HEXGEN-TEXT2SQL: Optimizing LLM Inference Request Scheduling for Agentic Text-to-SQL Workflow

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