AI's compute fragmentation: what matrix multiplication teaches us

The blog post discusses the challenges of compute fragmentation in AI, highlighting how the rapid growth in data and model complexity has outpaced the scalability of traditional computing hardware, leading to a reliance on specialized processors like GPUs and TPUs. This shift has fragmented the industry, making it difficult for developers to create software that fully utilizes diverse hardware capabilities and scales efficiently across various devices. A critical aspect of this challenge lies in optimizing matrix multiplication (matmul), which is foundational in machine learning models but varies widely across hardware configurations and data types. Despite advances in specialized hardware and optimized libraries, the lack of portability, scalability, and user-friendliness in current solutions limits their effectiveness, as they often require low-level assembly programming that doesn't easily adapt to new hardware or model variations. The post introduces Modular's initiative to rebuild AI infrastructure from the ground up to address these issues, promising more details in a subsequent blog post.