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NVIDIA Blog

What’s the ROI? Getting the Most Out of LLM Inference

Large language models and the applications they power enable unprecedented opportunities for organizations to get deeper insights from their data reservoirs and to build entirely new classes of applications. But with opportunities often come challenges. Both on premises and in the cloud, applications that are expected to run in real time place significant demands on Read Article Different scenarios have different requirements, and parallelism techniques bring optimal performance for each of these scenarios.

The Virtuous Cycle


Over the lifecycle of our architectures, we deliver significant performance gains from ongoing software tuning and optimization. These improvements translate into additional value for customers who train and deploy on our platforms. They’re able to create more capable models and applications and deploy their existing models using less infrastructure, enhancing their ROI.

As new LLMs and other generative AI models continue to come to market, NVIDIA will continue to run them optimally on its platforms and make them easier to deploy with technologies like NIM microservices and NIM Agent Blueprints.

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Published: 2024-10-09T15:00:32











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