https://techcrunch.com/2022/04/28/exafunction-aims-to-reduce-ai-dev-costs-by-abstracting-away-hardware/

The most sophisticated AI systems today are capable of impressive feats, from directing cars through city streets to writing human-like prose. But they share a common bottleneck: hardware. Developing systems on the bleeding edge often requires a huge amount of computing power. For example, creating DeepMind’s protein structure-predicting AlphaFold took a cluster of hundreds of GPUs. Further underlining the challenge, one source estimates that developing AI startup OpenAI’s language-generating GPT-3 system using a single GPU would’ve taken 355 years.

New techniques and chips designed to accelerate certain aspects of AI system development promise to (and, indeed, already have) cut hardware requirements. But developing with these techniques calls for expertise that can be tough for smaller companies to come by. At least, that’s the assertion of Varun Mohan and Douglas Chen, the co-founders of infrastructure startup Exafunction. Emerging from stealth today, Exafunction is developing a platform to abstract away the complexity of using hardware to train AI systems.