  In the upcoming decade, deep learning may revolutionize the natural sciences,
enhancing our capacity to model and predict natural occurrences. This could
herald a new era of scientific exploration, bringing significant advancements
across sectors from drug development to renewable energy. To answer this call,
we present DeepSpeed4Science initiative (deepspeed4science.ai) which aims to
build unique capabilities through AI system technology innovations to help
domain experts to unlock today's biggest science mysteries. By leveraging
DeepSpeed's current technology pillars (training, inference and compression) as
base technology enablers, DeepSpeed4Science will create a new set of AI system
technologies tailored for accelerating scientific discoveries by addressing
their unique complexity beyond the common technical approaches used for
accelerating generic large language models (LLMs). In this paper, we showcase
the early progress we made with DeepSpeed4Science in addressing two of the
critical system challenges in structural biology research.
