VMware and Nvidia are pioneering private AI solutions that allow companies to run custom AI models in their own data centers, enhancing job security and privacy.
- Fine-tuning AI models on a company’s proprietary data enables the creation of tailored AI solutions without sharing sensitive information with public models.
- Private AI models like ChatGPT can be run offline on personal devices, offering significant privacy and security advantages over cloud-based AI services.
Practical Applications
- Private GPT can be connected to a company’s knowledge base, IT procedures, and support tickets to provide customized assistance and troubleshooting for employees.
- Running private AI models on a laptop with a solar panel could provide access to AI-powered tools and information during emergencies or off-grid scenarios.
- RAG (Retrieval Augmented Generation) instructs AI to consult a connected database before answering questions, ensuring accuracy and relevance of responses.
Technical Implementation
- VMware’s Private AI Foundation with Nvidia offers a complete solution including deep learning VMs, Nvidia AI enterprise tools, and partnerships with Intel and IBM for diverse AI implementations.
- Prompt tuning, a technique for fine-tuning LLMs, can be completed in just 3-4 minutes by feeding additional prompts and answers to modify the model’s behavior.
- Data scientists can use VMware’s deep learning VMs and Nvidia’s tools for fine-tuning, while system engineers can implement the solution, streamlining the process for companies.
Future Outlook
- Private AI is positioned as the future of artificial intelligence, with VMware’s products making it increasingly accessible for organizations to implement locally.
- The ability to run private, offline AI models on personal devices could revolutionize how we interact with AI, potentially reshaping industries and personal computing.
- The combination of private AI and fine-tuning capabilities offers a new paradigm for organizations to leverage AI technology while maintaining control over their data and algorithms.