Advanced Features of RHEL AI

This chapter explores the advanced features of the “opinionated yet flexible” engineering that lets RHEL AI wring every token of performance—and every ounce of security—out of modern infrastructure. We begin with the raw silicon: Hopper-class NVIDIA Tensor Core H100 and AMD Instinct MI300X GPUs that deliver teraflops of mixed-precision throughput, high-bandwidth HBM3, and sub-millisecond interconnects. RHEL AI wraps these advantages inside a bootable immutable container (bootc) image that already carries CUDA, ROCm, kernel tunables, and a DeepSpeed-based training stack. Hence, the environment you boot is the exact one you build. No drift, no “works-on-my-GPU” headaches. On top of that immutable base, DeepSpeed provides ZeRO-3 parameter partitioning, MiCS communication scaling, and sparse-expert or NVMe-paged pipelines that enable a single 8-GPU node to fine-tune models with hundreds of billions of parameters or serve 60 tokens per second—in FP8—straight out of the box. The chapter then demonstrates how these same containers seamlessly integrate into AWS, Azure, GCP, and IBM Cloud, how security hardening begins with Secure Boot and concludes with signed model artifacts, and how Ansible roles transform the entire workflow into a declarative YAML format. The common thread is reproducibility: from kernel to checkpoint, every layer is versioned, signed, and portable.