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Machine learning models are getting larger and larger — some might even say, humongous. The world’s most advanced technology companies have been in an arms race to see who can train the largest model (MUM, OPT, GPT-3, Megatron), while other companies focused on production systems have scaled their existing models to great effect. Through all the excitement, what’s gone unsaid is the myriad of practical challenges larger models present for existing AI infrastructure and developer workflows.

AI promised to profoundly change the world, so why hasn’t it?From healthcare to manufacturing, finance, climate, communication, and travel, to how we live and work. AI can help solve any problem that can be represented by data, assuming the right algorithms and enough computational resources.

Platforms like TensorFlow, PyTorch, and CUDA do not focus on modularity - there, we said it! They are sprawling technologies with thousands of evolving interdependent pieces that have grown organically into complicated structures over time. AI software developers must deal with this sprawl while deploying workloads to server, mobile devices, microcontrollers, and web browsers using multiple hardware platforms and accelerators.