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Table Of Contents
LLMs in Enterprise
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As large language models (LLMs) continue to redefine artificial intelligence (AI) across industries, a critical challenge has emerged: the widening gap between theoretical capabilities and practical deployment. While much academic attention is given to innovations in training and architecture, the equally vital process of inference, the act of generating outputs from trained models, often operates behind the scenes; yet it dictates the feasibility, responsiveness, and cost-efficiency of real-world AI systems.
This chapter delves into the rapidly evolving domain of LLM inference optimization. We explore how specialized techniques and engines are reshaping deployment strategies, enabling everything from low-latency conversational agents to high-throughput batch systems. As the scale and complexity of models grow, so too must our strategies for executing them efficiently. From maximizing GPU utilization to fitting powerful models on...