Maximizing Efficiency in Large Language Models: Compute, Memory, and Fine-Tuning

From Twin Karmakharm July 24, 2024

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Speaker: Karin Sevegnani, Senior Solutions Architect, Nvidia

In this talk, we will explore the intricate balance between computational resources, memory limitations, and parameter-efficient fine-tuning techniques in large language models (LLMs). We will analyse strategies to optimize the performance of LLMs while managing these constraints effectively. From efficient memory utilization to streamlined parameter fine-tuning methods, we will discuss practical approaches to maximize the efficiency of LLMs without sacrificing performance.

Presented at Best Practices in AI Afternoon event 2024-07-05

Tags: artificial intelligencedeep learninglarge language modeloptimisationfine-tuning

Appears In: Research Software Engineering

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Maximizing Efficiency in Large Language Models: Compute, Memory, and Fine-Tuning

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