Home > AI Solutions > Gen AI > White Papers > Dell Scalable Architecture for Retrieval-Augmented Generation (RAG) with NVIDIA Microservices > Overview
The collaborative partnership between Dell Technologies and NVIDIA has resulted in a spotlighted solution for Generative AI Large Language Models (LLMs), focusing on the Llama 2 model. This model, developed by Meta and available for download from Meta or Hugging Face, is a crucial component of the solution. The deployment of the Llama 2 model within NVIDIA's cloud-native framework, NeMo, is explored, highlighting the combined strengths of both companies in advancing the field of Generative AI and LLMs.
Dell Technologies, recognized globally for leading digital transformation, offers a range of products that perfectly complement the NVIDIA RAG microservice. These products range from PowerEdge servers and networking to PowerScale storage and other infrastructure solutions designed for the data center. This integrated relationship enables the transformative NVIDIA RAG microservice framework to create best-in-class solutions on Dell Technologies' platforms, enhancing the accuracy and reliability of AI-driven workloads in data centers.
A path has been established for businesses to leverage this advanced AI technology from testing to production. The focus is on assisting teams in understanding and overcoming the challenges in deployment and scale. In partnership with NVIDIA, Meta, and Hugging Face, Dell enables customers to deploy LLMs on-premises, from desktop to cloud or desktop to data center, ensuring businesses can leverage the transformative power of AI wherever they are.
By leveraging NVIDIA AI Enterprise, businesses can access technical support for RAG on PowerEdge, which features optimized containers that enhance GPU accessibility. NVIDIA's microservices, including Kubernetes containers for RAG, training, and data curation, have been meticulously optimized at every step of the RAG process. This comprehensive optimization ensures businesses can deploy highly efficient, GPU-accelerated AI solutions, marking a significant advancement in AI-driven Technical Support and interactive applications.