RHEL AI, Red Hat's new solution for machine learning tasks

RHEL AI logo

Few days ago Red Hat announced, through a blog post, the launch of its new Linux distribution called “Red Hat Enterprise Linux AI” (RHEL AI), which is designed specifically for machine learning tasks.

RHEL AI simplifies the creation of server solutions that use large conversational modelssy provides a selection of tools and frameworks for machine learning. Also includes drivers for various hardware accelerators from AMD, Intel and NVIDIA, and components that leverage the capabilities of AI-optimized servers from Dell, Cisco, HPE, Lenovo and SuperMicro.


RHEL AI is designed for the development, testing and execution of machine learning systems based on the large Granite language model which can handle up to 4 thousand tokens and cover 7 billion parameters when generating text.

To interact with the Granite model, the distribution integrates InstructLab, which supports the LAB (Large Scale Alignment for ChatBots) methodology to customize and optimize models, as well as add additional knowledge and implement new skills to pre-trained models.

RHEL AI includes collaboratively developed, open source licensed, high-performance Granite language and code models from the InstructLab community, fully supported and indemnified by Red Hat. These Granite models are licensed under Apache 2 and provide transparent access to data sources and model weights.

Users can create their own customized LLM by training the base models with their own skills and knowledge. They can choose to share the trained model and added skills and knowledge with the community or keep them private. See more about that in the next section.

In the advertisement, it is mentioned that the main goal of RHEL AI and the InstructLab project is to train experts in the domain so that they directly contribute to large language models with knowledge and skills. This allow to experts in the field create AI-based applications more efficiently (like chatbots). RHEL AI includes everything you need:

  • Leverage community innovation through open source models and open source skills and knowledge for training.
  • Provide an easy-to-use set of software and workflow tools that targets domain experts without data science experience and enables them to perform training and adjustments.
  • Packaging software and operating system with optimized AI hardware enablement.
    business support and intellectual property compensation

In addition, it is mentioned that the platform can be used to develop AI applications for corporate needs and implement services to generate content, create dialogue systems and integrate virtual assistants into applications.

These apps can perform tasks such as answering questions in natural language, solving mathematical problems, generating meaningful text on a given topic, writing content summaries, correcting errors in texts, rewriting in other words, helping to write code in various programming languages, and generating letters and documents using templates.

On the other hand, it is also worth mentioning that Red Hat has introduced a new mode for creating and managing system images based on Red Hat Enterprise Linux: the "image mode". This mode allows you to use the tools and technologies used to create and run application containers in your operating system deployment.

The new mode handles monolithic system images, generated with rpm-ostree and updated atomically, without being split into separate packages. Builds can be generated as images in various formats, such as OCI (used in Docker), ISO, QCOW2, among other formats, and the content of the image is selected by editing the Containerfile.

To create and manage images, standard container management tools such as Podman and OpenShift Container Platform can be used. To install the images, you can use the standard Anaconda installer or bootc-image-builder, which allows you to convert a container image to a bootable disk image. Bootc is used to update container boot images that include the Linux kernel and can boot in the same way as normal system builds.

Finally, if you are interested in knowing more about it, you can check the details in the following link

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