Run your own AI (but private)
NetworkChuck・2 minutes read
Private AI is a system that allows users to run AI on their computer for data privacy, sponsored by VMware and offering pre-trained models like Llama two. Fine-tuning AI models with proprietary data for personalized applications is crucial for businesses and individuals interested in localized, secure AI systems.
Insights
- Private AI allows users to run AI on their computers without internet, ensuring data privacy and catering to individuals in roles with privacy restrictions.
- VMware sponsors Private AI, offering on-premises AI capabilities beyond cloud services, with tools like O Lama for installation and Nvidia GPUs for enhanced performance, emphasizing the importance of localized, private AI systems for businesses and individuals.
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Recent questions
What is Private AI and its main benefit?
Private AI is a self-contained system that allows users to run their own AI on their computer without an internet connection, ensuring data privacy. The main benefit of Private AI is that it is particularly beneficial for individuals in job roles where using external AI systems is restricted due to privacy and security concerns.
How can Private AI be set up and what are its advanced features?
Setting up Private AI is quick, easy, and free, taking approximately five minutes. Advanced features are available for those interested in exploring more capabilities of the system.
What are some examples of pre-trained AI models available for download?
Pre-trained AI models like Llama two are available for download from platforms like hugging face.co. These models have been trained on vast amounts of data and resources, making them valuable for various AI applications.
Who sponsors Private AI and what unique capabilities does it offer?
VMware sponsors Private AI, enabling companies to run their AI on-premises in their data centers. Private AI offers unique capabilities beyond cloud services, providing enhanced control and security for AI operations.
How can Private AI be installed and what hardware is recommended for optimal performance?
Installing Private AI involves using tools like O Lama, available for macOS and Linux, with Windows support coming soon. Utilizing Nvidia GPUs is recommended for enhanced performance when running AI models like Llama two on Private AI.
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Summary
00:00
Private AI: Secure, Localized, Customizable AI Solution
- Private AI is a self-contained system that allows users to run their own AI on their computer without internet connection, ensuring data privacy.
- Setting up Private AI is quick, easy, and free, taking approximately five minutes, with advanced features available for those interested.
- Private AI is particularly beneficial for individuals in job roles where using external AI systems is restricted due to privacy and security concerns.
- VMware sponsors Private AI, enabling companies to run their AI on-premises in their data centers, offering unique capabilities beyond cloud services.
- Private AI models, like Llama two, are pre-trained artificial intelligence models available for download from platforms like hugging face.co, with vast data and resources behind their training.
- Llama two, a large language model, was pre-trained by Meta (Facebook) on over 2 trillion tokens of data from public sources, costing an estimated $20 million and taking 1.7 million GPU hours.
- Installing Private AI involves using tools like O Lama, available for macOS and Linux, with Windows support coming soon, and utilizing Nvidia GPUs for enhanced performance.
- Running an AI model like Llama two on Private AI involves simple commands, with GPU support significantly improving processing speed and performance.
- Private AI's potential extends to fine-tuning AI models with proprietary data, enabling personalized and secure AI applications for various purposes, including customer interactions and troubleshooting.
- The future of AI lies in private, localized systems like Private AI, offering data privacy and customization options that are crucial for businesses and individuals.
10:28
Fine-tuning AI models with VMware and Nvidia
- Pre-training a model with Mata required 6,000 GPUs and 1.7 million GPU hours.
- Companies like VMware are working on fine-tuning models with internal, unreleased data.
- To fine-tune a model, hardware servers with GPUs and tools like PyTorch and TensorFlow are needed.
- VMware offers VMware Private AI with Nvidia, providing a comprehensive package for AI fine-tuning.
- The process involves using VMware's infrastructure, virtual machines, and deep learning VMs.
- Data scientists use tools like Jupyter notebooks to prepare data for training or fine-tuning models.
- Fine-tuning a model may require only 9,800 examples, a small amount compared to the original training data.
- Only 0.93% of the model's parameters are changed during fine-tuning, taking 3-4 minutes.
- VMware and Nvidia simplify the process of fine-tuning models with deep learning VMs and AI tools.
- RAG, a vector database, allows connecting an LLM to a knowledge base for accurate answers without retraining.
21:01
"VMware's Deep Learning VM Solution"
- VMware offers a deep learning VM as part of their solution, providing all the necessary tools for running a private local AI without the hassle of installing multiple tools individually.
