LLM Economics: The Cost of Leveraging Large Language Models

In this talk, Nikunj Bajaj, CEO & Cofounder of TrueFoundry, dives deep into the economic realities of deploying Large Language Models (LLMs) in production. The presentation explores the various costs involved in building LLM-based applications, comparing approaches like RAG (Retrieval Augmented Generation) versus fine-tuning, and contrasting the use of open-source models with commercial LLMs. Bajaj uses a concrete example of summarizing Wikipedia to illustrate the significant cost differences based on model choice and configuration, highlighting that a task could cost anywhere from $2,100 using a self-hosted 7 billion parameter model to $360,000 using GPT-4.

The talk also touches upon the quality trade-offs, the impact of context window size, and the cost-effectiveness of fine-tuning. It examines the performance of open-source models like Llama 2 against proprietary models like GPT-4, noting strengths in areas like named entity recognition and formatting, but weaknesses in coding and mathematical reasoning. Finally, Bajaj discusses the coexistence of open-source and commercial LLMs, the challenges of maintaining production-grade LLM applications beyond prototyping, and TrueFoundry's platform designed to simplify deployment, fine-tuning, and switching between various LLM providers.