NVIDIA has introduced its Inference Microservices (NIM) to enhance the deployment and performance of generative AI models. This new technology significantly reduces re-planning time from hours to seconds and supports large language models (LLMs) such as Meta's Llama 3 with 8 billion and 70 billion parameters. NIM can be deployed on various Kubernetes platforms, including those from Canonical, Nutanix, and Red Hat. Companies like Dataloop and Haystack AI are integrating NIM into their pipelines to optimize performance. The technology is designed to simplify, scale, and accelerate AI deployment, making it accessible to millions of developers worldwide. This announcement was made at COMPUTEX2024.
Allow users to deploy LLMs at scale with an API call ➡️to NVIDIA NIM on #opensource Kubernetes platforms from providers such as Canonical, Nutanix and Red Hat. Check out the blog to learn about the news from #COMPUTEX2024 https://t.co/zVT08n0aPv https://t.co/OmVfmjxSWu
Enhance efficiency and reduce operational costs by leveraging seamless deployment with NVIDIA NIM, starting with Meta’s Llama 3 70B & 8B, on your preferred cloud service provider, directly accessible from Hugging Face. ➡️ https://t.co/YCBQE3jhmg. https://t.co/ReCI98bBPj https://t.co/mLw6E1gzR4
See how NVIDIA NIM inference microservices can help organizations simplify, scale, and accelerate #generativeAI deployment. Watch this video and transform your AI deployment into a production-ready powerhouse. https://t.co/tXfRxILs1k https://t.co/BKyL9TJBad
🎉 You can now access NIM inference microservices for 8B and 70B parameter @Meta Llama 3 models for self-hosted deployment on your choice of NVIDIA accelerated infrastructure 🎊 ➡️ https://t.co/rzUgakwnBc #COMPUTEX2024 https://t.co/WJPILTczB7
NVIDIA NIM Revolutionizes Model Deployment, Now Available to Transform World’s Millions of Developers Into Generative AI Developers #DL #AI #ML #DeepLearning #ArtificialIntelligence #MachineLearning #ComputerVision #NeuroMorphic #Robotics https://t.co/6hWVFLGCVC
🚀 Just released a comprehensive benchmark on #LLM #inference backends (https://t.co/8FkXrjtuAc)! Essential for every AI/ML engineer to know how these tools can enhance the performance & reliability of LLMs. In our benchmark study, we evaluated the serving performance of #Llama3…
🚀 Just released a comprehensive benchmark on #LLM #inference backends! Essential for every AI/ML engineer to know how these tools can enhance the performance & reliability of LLMs. 📊🔗 https://t.co/8FkXrjtuAc In our benchmark study, we evaluated the serving performance of…
Use of Self-hosted @nvidia NIMs in the RAG Pipeline with @Haystack_AI looks neat. The example code is for using a previously deployed NIMs on your infrastructure in a Kubernetes cluster. NIMs (NVIDIA Inference Microservices) allow you to reach optimal performance on NVIDIA… https://t.co/5qF7Nmvzg6
SaySelf: A Machine Learning Training Framework That Teaches LLMs To Express More Accurate Fine-Grained Confidence Estimates https://t.co/X8DIm0LcKf #LLMs #SaySelf #AI #confidenceestimations #automation #ai #news #llm #ml #research #ainews #innovation #artificialintelligence #… https://t.co/rRofeKNi1I
Allow users to deploy LLMs at scale with an API call with NVIDIA NIM on #opensource Kubernetes platforms from providers such as @Canonical, @Nutanix and @RedHat. ➡️ https://t.co/NXiV6yciOT Check out the blog to learn about the news from #COMPUTEX2024 https://t.co/k7CbQFGDqk
If you are building LLM Apps with open LLMs and want to deploy them yourself, optimization of software and hardware stack is a pain. @nvidia just released NIM, their Inference Microservices for AI Models. Learn more here: https://t.co/q92IvB34H8
Dataloop Integrates NVIDIA NIM to Accelerate Running and Deploying Generative AI -- https://t.co/TaIKUP1kKa #AI #GenAI @DataloopAI @NVIDIAAI
Using generative AI, NVIDIA operations built an AI Planner agent, developed on NVIDIA Inference Microservices (NIM). The agent leverages LLM, NeMo Retriever and CuOpt NIM to reduce re-planning time from hours to just seconds. https://t.co/GMPGdPfbOu https://t.co/tYTTS9Upob https://t.co/Wl2SUfknia