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How Full-Stack NIM Optimizations Deliver 2.5x More Users on Nemotron 3 Ultra

How Full-Stack NIM Optimizations Deliver 2.5x More Users on Nemotron 3 Ultra

二〇二六年九月十日 · 英文原文

Deploying a large language model is only the first step toward production-ready serving. Production teams also need to serve as many concurrent users as... Deploying a large language model is only the first step toward production-ready serving. Production teams also need to…

Deploying a large language model is only the first step toward production-ready serving. Production teams also need to serve as many concurrent users as... Deploying a large language model is only the first step toward production-ready serving. Production teams also need to serve as many concurrent users as possible on available GPU infrastructure while preserving the interactivity that keeps applications responsive. That tradeoff matters even more for agentic AI workloads, where prompts can be long, context can be reused across steps… Source

译自 NVIDIA · Developer 博客 · 录于 二〇二六年九月十日