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NVIDIA Blueprint: PDF Ingestion
NVIDIA Blueprints: PDF Ingestion, also known as NVIDIA-Ingest, or NV-Ingest, this blueprint is a scalable, performance-oriented document content and metadata extraction microservice. Including support for parsing PDFs, Word and PowerPoint documents, it uses specialized NVIDIA NIM microservices to find, contextualize, and extract text, tables, charts and images for use in downstream generative applications.
Sandbox Lab
• 40 launches
NVIDIA Blueprint: Generative Virtual Screening for Drug Discovery
The NVIDIA Blueprints for generative virtual screening demonstrate how generative AI and accelerated NVIDIA NIM microservices can be leveraged to design optimized small molecules more intelligently and efficiently.
Sandbox Lab
• 101 launches
Overview of NVIDIA NIM Microservices
Welcome to part 2 about our RAG lab infrastructure built in collaboration with NetApp, NVIDIA, and WWT. NVIDIA NIM microservices is a suite of user-friendly microservices that facilitate the deployment of generative AI models, such as large language models (LLMs), embeddings, re-rankings, and others, across various platforms. NVIDIA NIM microservices simplify the process for IT and DevOps teams to manage LLMs in their environments, providing standard APIs for developers to create AI-driven applications like copilots, chatbots, and assistants. It leverages NVIDIA's GPU technology for fast, scalable deployment, ensuring efficient inference and high performance.
Video
• 3:29
• Aug 28, 2024
Accelerate your AI Journey with NVIDIA NIM
Transform AI model deployment with best-of-breed technology without sacrificing control or privacy.
Blog
• Aug 26, 2024
The NetApp and NVIDIA Infrastructure Stack
Welcome to part 1 of the videos series about the RAG Lab Infrastructure built in collaboration with NetApp, NVIDIA, and World Wide Technology. This video series will take you behind the scenes of this state-of-the-art lab environment inside the AI Proving Ground from WWT, powered by the Advanced Technology Center.
Video
• 4:45
• Aug 23, 2024