The adoption of Arti cial Intelligence (AI) models running locally on your own computers is becoming increasingly common, o ering signi cant security and privacy benefits. Running AI locally allows you to keep sensitive data within your own environment, reducing the risks associated with transmitting information over external networks. However, this setup also presents some challenges, including the need for powerful hardware and adequate storage space.

Benefits of On-Premise AI

• Data Security: Running AI models locally ensures that data stays within your system, providing complete control over sensitive information. This is especially important for businesses and professionals who handle confidential data.
• Personalization: Local implementation allows for greater customization of AI models, adapting them to the specific needs of the user or organization.

Challenges of AI in Local

• Hardware Requirements: Running advanced AI models requires a dedicated, high-performance GPU. Graphics cards such as NVIDIA RTX 30 or 40 series, or the latest generation AMD Radeon, are often required to handle these workloads effectively. Additionally, at least 16GB of RAM is recommended for smooth performance.
• Storage Space: AI models can take up several
  gigabytes of disk space. For example, models like the Llama3 can require up to 11 GB of space. It is therefore essential to ensure that you have su cient SSD space to install and run the desired models.

Available Tools and Templates

• NVIDIA ChatRTX: A demo application that lets you customize a GPT Large Language Model (LLM) linked to your own content, such as documents, notes, images, or other data. It uses the
  Retrieval-Augmented Generation (RAG) technology and RTX acceleration to deliver relevant answers quickly and securely, all running locally on a Windows PC or workstation with RTX GPUs.
• Llama3 by Meta: A family of large language models developed by Meta, available in several sizes, including 8B and 70B parameters. These models are optimized for use in dialogs and can run locally on suitable hardware. For example, the Llama3-ChatQA-1.5-70B model was developed to excel at conversational question answering and retrieval-augmented generation
• Ollama: An open-source framework that facilitates running models like Llama3 on local systems. Ollama allows you to download and manage multiple AI models, providing an interface to chat with AI directly on your PC. It is compatible with multiple LLM models and supports running on hardware with the appropriate specifications.

Final Considerations
Running AI models locally gives you greater control over your data and the ability to customize solutions to your needs. However, it is essential to carefully evaluate the hardware and storage requirements needed to ensure a smooth experience.
optimal. As technology continues to evolve, tools like NVIDIA ChatRTX, Llama3, and Ollama are invaluable resources for those looking to deploy AI solutions locally.