Reducing the Barriers to Entry for Foundation Model Training

TL;DR

This paper discusses the urgent need for a fundamental overhaul of AI training infrastructure to address rising demands, focusing on hardware, software, and energy efficiency to lower barriers for large language model development.

Contribution

It introduces an analytical framework that quantifies challenges and opportunities for reducing entry barriers in large language model training through technological advancements.

Findings

Highlights the increasing strain on current AI infrastructure

Identifies key areas for technological improvements

Proposes a framework to evaluate reduction strategies

Abstract

The world has recently witnessed an unprecedented acceleration in demands for Machine Learning and Artificial Intelligence applications. This spike in demand has imposed tremendous strain on the underlying technology stack in supply chain, GPU-accelerated hardware, software, datacenter power density, and energy consumption. If left on the current technological trajectory, future demands show insurmountable spending trends, further limiting market players, stifling innovation, and widening the technology gap. To address these challenges, we propose a fundamental change in the AI training infrastructure throughout the technology ecosystem. The changes require advancements in supercomputing and novel AI training approaches, from high-end software to low-level hardware, microprocessor, and chip design, while advancing the energy efficiency required by a sustainable infrastructure. This paper presents the analytical framework that quantitatively highlights the challenges and points to the opportunities to reduce the barriers to entry for training large language models.