The OPU is Tsavorite’s architecture for AI compute, designed to be composable from the ground up. Instead of a single fixed chip, it treats compute, memory, and interconnect as resources that can be assembled and scaled to match a workload. This means one architecture can serve models across very different environments, from the edge to the datacenter, adapting to what you run rather than forcing your workload to fit fixed silicon.

A GPU gives you a fixed ratio of compute, memory, and bandwidth, and every workload has to fit within it. The OPU takes a composable approach. Its compute, memory, and interconnect are designed to scale independently, so you can provision for the model in front of you instead of over-buying one dimension to get enough of another. The intent is to improve utilization and efficiency across a wider range of AI workloads, from inference to training.

Composable means the core building blocks of compute are not locked into one fixed configuration. In the OPU, resources such as compute, memory, and interconnect can be combined and scaled to fit a given workload, then reconfigured as needs change. In practice, this lets a single architecture span many deployments and grow over time, so you are not tied to one point design or forced to replace the platform each time your models get larger.

MultiPlexus is the fabric that ties the OPU together, and it operates at every level of the system. It begins as a coherent, high-performance network on chip with distributed caches, then extends over UCIe to connect all chiplets in a package, over SERDES to connect all packages in a system, and scales up further across systems and racks to form unified memory pods. This any-to-any fabric is what makes composability real. As it scales up – more compute and memory operate as one – raising arithmetic intensity and AI compute utilization.

The OPU is built for modern AI workloads across a wide range of environments, from on-premises and edge deployments to larger datacenter systems. It targets use cases where efficiency, scale, and flexibility all matter, including inference and training for large models. Because the architecture is composable, the same platform can be tuned for different points along that spectrum. For details on a specific workload, deployment option, or availability, reach out to our team.