RHEL AI: Try out LLMs the easy way

Learning path | 4 resources | Published on September 5, 2024
Prasanth Anbalagan

Learn how Red Hat Enterprise Linux AI provides a security-focused, low-cost environment for experimenting with large language models (LLMs).

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  1. Overview: RHEL AI: Try LLMs the easy way
  2. How are LLMs created? Page
  3. Understanding LLM workflows Page
  4. Optimizing LLMs for accuracy Page

Overview: RHEL AI: Try LLMs the easy way

RHELAI featured image

Often, users want to experiment with large language models (LLMs) before developing and scaling out to a much larger production environment. However, experimenting with popular LLM-based applications like ChatGPT means sharing sensitive personal information, which often raises concerns about how user data is stored and used. 

Red Hat Enterprise Linux (RHEL) AI addresses this challenge by providing a low-cost, security-focused, single-server environment to experiment with large language models. RHEL makes it easy for users with little to no data science expertise to start developing and enhancing large language models without any data privacy or security concerns. 

Prerequisites

In this learning path, you will:

  • Understand how LLMs are created.
  • Dive into the LLM workflow.
  • Use RHEL AI to experiment within an LLM.