Optimizing Inference Efficiency for LLMs at Scale with NVIDIA NIM Microservices – NVIDIA Technical Blog News and tutorials for developers, data scientists, and IT admins 2025-03-27T16:00:00Z http://www.open-lab.net/blog/feed/ Rajvir Singh <![CDATA[Optimizing Inference Efficiency for LLMs at Scale with NVIDIA NIM Microservices]]> http://www.open-lab.net/blog/?p=87091 2024-08-22T18:24:55Z 2024-08-14T19:30:00Z As large language models (LLMs) continue to evolve at an unprecedented pace, enterprises are looking to build generative AI-powered applications that maximize...]]> As large language models (LLMs) continue to evolve at an unprecedented pace, enterprises are looking to build generative AI-powered applications that maximize...

As large language models (LLMs) continue to evolve at an unprecedented pace, enterprises are looking to build generative AI-powered applications that maximize throughput to lower operational costs and minimize latency to deliver superior user experiences. This post discusses the critical performance metrics of throughput and latency for LLMs, exploring their importance and trade-offs between��

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