Reducing the Barriers to Entry for Foundation Model Training
This addresses the problem of high costs and resource limitations for organizations and researchers seeking to train large language models, though it is incremental as it builds on existing infrastructure challenges.
The paper tackles the escalating costs and resource demands of training foundation models by proposing a fundamental overhaul of AI training infrastructure, identifying opportunities to reduce barriers to entry through quantitative analysis.
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.