How the NVIDIA Vera Rubin Platform is Solving Agentic AI’s Scale-Up Problem | NVIDIA Technical Blog

This post details how the NVIDIA Vera Rubin Platform, leveraging LPU C2C technology, addresses the scale-up problem for agentic AI workloads. It introduces the concept of extreme co-design where networking fabric is designed with silicon, compiler, and serving stack to achieve predictable, low-latency, and high-throughput communication across thousands of accelerators. Key technical contributions include: high-radix point-to-point links (96 C2C links at 112 Gbps per LPU), compiler-scheduled data movement (statically planned transfers of 320-byte vectors), and hardware-driven plesiosynchronous timing to cancel clock drift and align LPUs. This enables rack-scale determinism with a unified 128 GB on-chip SRAM pool, allowing trillion-parameter models to run at low latency without compromising context window or accuracy, and bounding tail latency under bursty fan-out patterns.