By Seamus Jones and Ajay Mungara
The newly launched Dell Generative AI Solutions with Intel, powered by Dell PowerEdge XE9680 and Intel® Gaudi® 3 accelerators, deliver high-performance generative AI capabilities with optimized total cost of ownership (TCO), scalability, and flexibility in choosing the right hardware, software, and networking for your AI workflows.
At the heart of this collaboration, we're excited to introduce Intel AI for Enterprise RAG – a retrieval-augmented generation (RAG) software catalog with deployment-ready use cases tailored specifically for enterprise environments and powered by the Open Platform for Enterprise AI (OPEA). Available by year-end, this catalog provides a suite of RAG solutions designed to meet key enterprise needs, securely and cost-effectively.
Why this matters for your business
- Deploy enterprise-grade AI without compromising security
- Optimize infrastructure costs while maximizing performance
- Maintain flexibility with open-source solutions
- Start generating value from your data immediately
- Scale seamlessly as your needs evolve
Understanding RAG: The bridge to intelligent enterprise data
Retrieval-augmented generation enhances large language models (LLMs) by enabling them to generate responses based on your specific enterprise data. This approach ensures your AI solutions deliver contextually relevant results while keeping sensitive data secure. Powered by Dell PowerEdge XE9680, Intel Gaudi 3 AI accelerators, and Intel® Xeon® processors, our validated RAG solutions help you harness your data today while remaining adaptable for tomorrow's AI innovations.
Powerful infrastructure + open-source ecosystem for enterprise AI
The Dell PowerEdge XE9680 sets a new standard for AI infrastructure. This 2-socket, 6U air-cooled rack server is purpose-built for today's most demanding AI models. And, by integrating Intel Gaudi 3 AI accelerators, we're providing a cost-effective alternative to traditional GPUs, enabling broader AI adoption while maintaining enterprise-grade performance.
On top of this powerful infrastructure, our solutions leverage an open-source ecosystem that puts you in control, with a comprehensive stack – including vLLM, PyTorch, Hugging Face, LangChain, and Redis Vector database – and integrated, enterprise-grade microservices from OPEA, a Linux Foundation platform.
Enterprise-ready RAG solutions
1. Chat Question and Answer (Q&A)
- Capability: Develop a secure AI chatbot for large-scale chat-based Q&A, capable of processing text from a variety of document types (PDF, PPT, HTML, etc.).
- Example applications:
- Resolve common queries or route to appropriate resources
- Onboard and train new employees
2. Audio Q&A
- Capability: Deliver audio Q&A interactions within an enterprise context while processing audio data, ensuring user and data security and scaling to accommodate thousands of users.
- Example applications:
- Assist employees with disabilities by providing an alternate means of communication.
- Provide customers that call with immediate support and accurate responses, reducing wait times and improving customer satisfaction.
3. Visual Q&A
- Capability: Create image Q&A interactions using various data formats, including text and image content, ensuring user and data security and scaling to accommodate thousands of users.
- Example applications:
- Ensure quality assurance through image inspection for product deviations.
- Manage inventory levels through image analysis.
4. Content Summarization
- Capability: Securely summarize content from multiple data formats at scale.
- Example applications:
- Transcribe and summarize meeting notes.
- Extract insights from corporate disclosures.
5. Frequently Asked Question (FAQ) Generation
- Capability: Generate secure, scalable FAQs from various types of enterprise data, optimized for high user volume and compute efficiency.
- Example applications:
- Provide human resource department support.
- Assist busy front-line staff.
6. Code Generation
- Capability: Automate code generation to adhere to best practices, reduce human error, and reduce technical debt.
- Example applications:
- Automatically complete code snippets based on retrieved documentation.
- Suggest code modifications based on historical fixes or patches.
7. Code Translation
- Capability: Automate code translation from one programming language to another while maintaining code efficiency and reducing human error.
- Example applications:
- Conduct legacy system modernization.
- Facilitate large-scale code migration.
Start your RAG journey today
1. Access our resources
- Download the Enterprise RAG Deployment Guide
- Explore use case-specific implementation guides
- Browse the Intel AI Software Catalog
2. Connect with experts
- Schedule a solution demonstration
- Consult with Dell's technical team
- Join the Dell developer community
Learn more
Ready to transform your enterprise with AI? Contact the Dell solutions team at enterprise-ai@dell.com.
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