How Full-Stack NIM Optimizations Deliver 2.5x More Users on Nemotron 3 Ultra | NVIDIA Technical Blog

This post details how full-stack NIM optimizations, including autotuned kernels, tensor parallelism, prefix and state reuse, scheduler and memory tuning, and MTP speculative decoding, deliver up to 2.5x higher system throughput for Nemotron 3 Ultra on a 4xB200 system compared to a baseline serving stack. It introduces the concept of a Pareto chart for evaluating performance trade-offs between interactivity and throughput, and provides guidance on benchmarking NIM with NVIDIA AIPerf for representative workloads. It also outlines the deployment process for Nemotron 3 Ultra NIM, including selecting optimized profiles for agentic workloads.