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

DevFeed: [How Full-Stack NIM Optimizations Deliver 2.5x More Users on Nemotron 3 Ultra](<https://devfeed.tech/articles/how-full-stack-nim-optimizations-deliver-2-5x-more-users-on-nemotron-3-ultra-6840.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/how-full-stack-nim-optimizations-deliver-2-5x-more-users-on-nemotron-3-ultra/>)

Author: Elizabeth Goodman

Published: 2026-09-10T16:55:32Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [Language models](<https://devfeed.tech/topics/language-models.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [build-ai-agents](<https://devfeed.tech/tags/build-ai-agents.md>), [cache](<https://devfeed.tech/tags/cache.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [inference](<https://devfeed.tech/tags/inference.md>), [inference-performance](<https://devfeed.tech/tags/inference-performance.md>), [latency](<https://devfeed.tech/tags/latency.md>), [mamba](<https://devfeed.tech/tags/mamba.md>), [mixture-of-experts](<https://devfeed.tech/tags/mixture-of-experts.md>), [nemotron](<https://devfeed.tech/tags/nemotron.md>), [nim](<https://devfeed.tech/tags/nim.md>), [nvidia-blackwell](<https://devfeed.tech/tags/nvidia-blackwell.md>), [optimization](<https://devfeed.tech/tags/optimization.md>)

## AI overview

The article explains how NVIDIA NIM bundles serving optimizations to improve throughput for Nemotron 3 Ultra while meeting latency targets on GPU infrastructure.

## Source excerpt

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...