From Core to Rack: Scaling AI Workloads with Semidynamics

As artificial intelligence applications—particularly Large Language Models (LLMs) and complex generative AI frameworks—continue to grow exponentially in size, the infrastructure required to support them is hitting critical operational bottlenecks. Traditional compute architectures are increasingly struggling to keep pace with the massive memory capacities, high bandwidth requirements, and stringent power envelopes demanded by today's advanced datacenters. In this comprehensive session, we will explore how fundamentally rethinking compute architecture—from the initial silicon logic up to the fully integrated, high-capacity rack—can overcome these systemic industry challenges.
The presentation begins by diving into the foundational element of our ecosystem: the compute core. We will discuss how highly customizable RISC-V IP, seamlessly integrated with advanced vector and tensor units, provides the specialized compute necessary for demanding workloads. A pivotal focus will be on our proprietary technology, designed specifically to tolerate immense memory latency. By ensuring that processing units remain constantly fed, this core-level innovation effectively dismantles the infamous "memory wall" that restricts the performance of conventional processors.
However, the true breakthrough of the Semidynamics AI Rack lies in its extraordinary memory capacity. A core focus of this talk will be demonstrating how outfitting our racks with significantly more memory than alternative industry solutions completely redefines the datacenter power and bandwidth equation. Running massive, next-generation LLMs typically requires continuously shuttling terabytes of weight data and KV caches across expensive, power-hungry networks. By keeping vast amounts of data localized within the rack, our high-memory architecture drastically reduces off-node bandwidth requirements and slashes the massive power consumption normally wasted on data movement. Ultimately, this unprecedented on-rack memory capacity is exactly what enables datacenters to deploy much larger LLMs efficiently within a single, highly efficient footprint.
This session will demonstrate that true AI scalability is not achieved simply by stacking more generic compute hardware; it requires a tightly coupled engineering approach that prioritizes data locality and power efficiency. By the end of this presentation infrastructure executives will gain actionable insights into how Semidynamics’ unified design philosophy delivers unparalleled throughput. Join us to discover how harmonizing the microscopic intricacies of the core with a massive, memory-first AI Rack architecture will power the next generation of datacenter computing.