Key insights

• ⚙️ Private AI provides full control and privacy by running on own computer.
• 🔒 Companies benefit from private AI for privacy and security reasons.
• 💡 AI models are pre-trained on large datasets with significant resources and costs involved.
• 📶 A powerful AI model can be downloaded without internet and fine-tuned, using a tool called O Lama and installing it on macOS, Linux, or Windows using WSL.
• 🚀 Fine-tuning AI models with a GPU for 19 hours enables unrestricted questioning and better performance.
• 🛡️ The concept of fine-tuning AI models provides increased privacy and data control, making it the future of AI for companies and individuals.
• 💻 VMware and Nvidia provide tools for fine-tuning and deploying custom LLMs, including prompt tuning and RAG for retrieval-augmented generation, with partnerships with Intel and IBM offering options for data scientists and administrators.
• 📝 Setting up a private GPT with RAG and NVIDIA GPU on a Windows PC using WSL, accessing it through a web browser, and interacting with documents for information demonstrates the potential of Private AI with VMware.

Q&A

• How can private GPT with RAG and NVIDIA GPU be set up on a Windows PC?

  A private GPT with RAG and NVIDIA GPU can be set up on a Windows PC using WSL. After installation, it can be accessed through a web browser, allowing interaction with documents for information retrieval and other AI-related tasks.

• What tools are provided by VMware and Nvidia for fine-tuning and deploying custom LLMs?

  VMware and Nvidia provide tools for fine-tuning and deploying custom LLMs, including a technique called prompt tuning and a tool called RAG for retrieval-augmented generation. These tools allow for more efficient and accurate customization of language models, enhancing their performance and applicability.

• How can VMware's private AI with Nvidia benefit data scientists and administrators?

  VMware's private AI with Nvidia provides bundled tools for easier implementation and fine-tuning of language models, making the process more accessible and efficient. Additionally, it offers partnerships with Intel and IBM, providing options for data scientists and administrators to optimize AI capabilities.

• What practical applications are possible by fine-tuning AI models?

  Fine-tuning AI models enables customization and adaptation to specific data and use cases. This can be applied to various scenarios such as help desk support, troubleshooting code, customer interactions, and more. It also ensures increased privacy and data control, making it the future of AI for companies and individuals.

• Can I download a powerful AI model without internet and fine-tune it?

  Yes, a powerful AI model can be downloaded without internet and fine-tuned using a tool called O Lama. You can install it on macOS, Linux, or Windows using WSL, and then fine-tune the model for better performance, particularly by using a GPU to enhance its capabilities.

• How are AI models pre-trained and what are the associated costs?

  AI models are pre-trained on large datasets with significant resources and costs involved. This involves extensive computing power, time, and often the utilization of massive datasets to train the models effectively.

• How can companies benefit from private AI?

  Companies can benefit from private AI for privacy and security reasons. It allows them to have complete control over their AI models and data, preventing unauthorized access and ensuring confidentiality.

• What is the benefit of running private AI on your own computer?

  Running private AI on your own computer allows for full control and privacy, ensuring that sensitive data and AI models are not accessed or controlled by external entities.

• 00:00 Running private AI on your own computer allows for full control and privacy. Companies can benefit from private AI for privacy and security reasons. AI models are pre-trained on large datasets with significant resources and costs involved.
• 03:39 A powerful AI model can be downloaded without internet and fine-tuned, and the process involves using a tool called O Lama and installing it on macOS, Linux, or Windows using WSL. The model can then be run using GPU for better performance.
• 07:10 The video segment discusses the potential of running AI models locally, the ability to fine-tune AI models, and the impact on privacy and data control. It also explores the practical applications of fine-tuning AI models for specific use cases.
• 10:55 VMware wanted internal access to unreleased vSphere 8 update. Fine-tuning an LLM requires hardware, tools, and resources. VMware's private AI with Nvidia bundles tools for easier implementation. Data scientist uses vSphere, deep learning VM, Nvidia GPUs, and Jupyter notebook to fine-tune model with 9,800 examples.
• 14:46 VMware and Nvidia provide tools for fine-tuning and deploying custom LLMs, including a technique called prompt tuning and a tool called RAG for retrieval-augmented generation. They also offer partnerships with Intel and IBM, providing options for data scientists and administrators.
• 18:37 A YouTuber sets up a private GPT with RAG and NVIDIA GPU on a Windows PC using WSL, accesses it through a web browser, and interacts with documents for information. Private AI with VMware is praised for its potential. VMware private AI solution is recommended for bringing AI to companies. Free coffee quiz sponsored by Network Chuck Coffee is offered at the end of the video.