Peking University and StepFun Unveil TensorCast: A Programmable Tensor Management Layer That Cuts LLM Time-to-First-Token by Up to 93.2%

For years, optimizing large model inference systems has focused on enhancing GPU computations for tensors. However, as model sizes increase, the infrastructure now faces a challenge in managing tensor states across various system components. Peking University, StepFun, and Beijing University of Posts and Telecommunications have proposed TensorCast, a unified and programmable tensor lifecycle management solution that decouples tensor states from specific systems. This allows for the reuse of unified management capabilities across different workloads.