AI workloads with Red Hat Enterprise Linux

Red Hat Enterprise Linux provides a consistent path for AI hardware enablement by delivering validated AMD, Intel, and NVIDIA accelerator drivers and toolkits—including open stacks like ROCm and proprietary NVIDIA software such as CUDA—through Red Hat repositories. This approach reduces the complexity of manual patching and kernel conflicts. 

For NVIDIA hardware, Red Hat Enterprise Linux for NVIDIA is engineered to prioritize rapid innovation, so organizations can immediately use the latest AI advancements. This allows the OS to serve as the consistent, high-performance base for your AI strategy. It provides essential hardware enablement for next-generation NVIDIA accelerators, and acts as the trusted engine behind Red Hat AI Factory with NVIDIA and the broader portfolio, making deployment at scale possible across the hybrid cloud.

Red Hat Enterprise Linux supports the integrity of your AI infrastructure by extending enterprise-grade security from the kernel to the application layer. With built-in support for confidential computing, Red Hat Enterprise Linux lets you process sensitive data in trusted execution environments (TEEs). This shields proprietary datasets from unauthorized access while in use, even from the underlying host or hypervisor.

With built-in security controls, scanning workflows, and ongoing compliance checks, Red Hat Enterprise Linux can help reduce the attack surface  your AI workloads run on. This can help you establish a hardened foundation to train and deploy models, strengthen compliance controls for demanding regulatory requirements, and better protect your intellectual property.

AI models are especially sensitive to your software configuration. Red Hat Enterprise Linux can help reduce environmental drift by providing configuration guardrails and policies to support a consistent foundation across the hybrid cloud. Using the Red Hat Enterprise Linux image builder, teams can create golden images that package the specific kernel, drivers, and AI frameworks into a single, reproducible unit.

Whether deploying on-premises or in the public cloud, Red Hat Lightspeed proactively monitors these images for configuration drift, helping to ensure the production environment mirrors development. This standardized approach reduces technical discrepancies between environments, allowing organizations to scale AI workloads rapidly while lowering the risk of unpredictable model behavior or performance degradation caused by underlying OS variations.