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Chapter 3. New features and enhancements


This section describes new features and enhancements in Red Hat OpenShift AI 3.3.

3.1. New features

Model serving support for IBM Spyre AI accelerators on IBM Power

Model serving with IBM Spyre AI accelerators is now Generally Available (GA) on the IBM Power platform. The IBM Spyre Operator automates the installation and integration of key components, including the device plugin, secondary scheduler, and monitoring tools.

For more information, see the IBM Spyre Operator - Red Hat Ecosystem Catalog.

Allow and disallow functionality added to the model catalog
The model catalog now provides an administrative capability in the OpenShift AI dashboard to selectively hide, disallow list, or remove specific models from the visible catalog. This new feature ensures compliance with internal security, policy, or regulatory restrictions.
Kubeflow Trainer v2

Kubeflow Trainer v2 is Generally Available (GA) in Red Hat OpenShift AI 3.3.

Kubeflow Trainer v2 is the next generation of distributed training for OpenShift AI, replacing the Kubeflow Training Operator v1 (KFTOv1). This Kubernetes-native solution simplifies how data scientists and ML engineers run PyTorch training workloads at scale using a unified TrainJob API, pre-built ClusterTrainingRuntimes, and the Kubeflow Python SDK.

3.2. Enhancements

Updated naming of resources to include data-science prefix
To ensure a consistent naming convention across the product, resource naming is now updated to include the data-science- prefix.
vLLM-Gaudi 1.23 support
Red Hat OpenShift AI 3.3 now supports vllm-gaudi version 1.23, enhancing, enhancing performance and stability of vLLM applications.
Model catalog performance data with advanced search and filtering

The model catalog provides comprehensive model validation data, including performance benchmarks, hardware compatibility, and other relevant metrics for Red Hat validated third-party models.

This includes advanced search and filtering, for example, on throughput and latency for benchmarked hardware profiles, so users can quickly find validated models for their use case and available resources. This feature provides a unified discovery experience for models in the Red Hat OpenShift AI hub.

Configuring AuthN/AuthZ for llm-d

A documentation guide for configuring Authentication (AuthN) and Authorization (AuthZ) for Distributed Inference with llm-d is now available. This guide ensures that users can configure Distributed Inference workloads to be protected against unauthorized access and lateral movement within the cluster.

This is a documentation update only. Llm-d functionality remains unchanged.

Comprehensive High Performance Networking Guide for RDMA over Converged Ethernet (RoCE)

A documentation guide provides the roadmap for establishing high-availability, production-grade Distributed Inference with llm-d environments using RoCE. This guide decouples the complexities of high-performance networking to ensure your multi-GPU fabric remains lossless and stable, maximizing TFLOPS (Tera Floating-Point Operations Per Second) efficiency and minimizing tail-latency at scale.

This is a documentation update only. Llm-d functionality remains unchanged

Red Hat Operator catalogs moved from OperatorHub to the software catalog in the console

In OpenShift 4.20, the Red Hat-provided Operator catalogs have moved from OperatorHub to the software catalog and the Operators navigation item is renamed to Ecosystem in the console. The unified software catalog presents Operators, Helm charts, and other installable content in the same console view.

  • To access the Red Hat-provided Operator catalogs in the console, select Ecosystem Software Catalog.
  • To manage, update, and remove installed Operators, select Ecosystem Installed Operators.

For more information, see Red Hat Operator catalogs moved from OperatorHub to the software catalog in the console

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