Red Hat OpenShift AI

Discover the challenges of running AI in production at enterprise scale. Java alone is not enough, as AI workloads require a platform designed for scale. Red Hat OpenShift AI provides the operational backbone, extending Kubernetes with capabilities for building, deploying, and managing AI workloads. It ensures governance, observability, and portability, aligning with open standards. This allows Java developers to treat AI workloads with the same discipline as other enterprise services, making AI viable at enterprise scale. IBM and Red Hat are investing in open standards and products to secure the future of enterprise Java and AI.

Learn how the Java ecosystem is adapting to the AI era and how IBM and Red Hat are investing to secure its future.

InstructLab empowers developers to unleash the full potential of LLMs, offering a streamlined training process, cost-efficiency, community collaboration, and stability in model performance.

An article series that describes key challenges in AI infrastructure: optimizing resource usage (particularly GPUs), reducing costs, and enabling scalability. This series introduces NVIDIA Multi-Instance GPU (MIG) technology, which enables more efficient utilization of GPUs resources for AI and ML workloads in Red Hat OpenShift AI and Kubernetes.

The integration of NVIDIA MIG technology with Red Hat OpenShift provides a powerful solution for optimizing GPU resources in containerized environments. By enabling fine-grained GPU partitioning, organizations can significantly improve resource utilization, enhance workload isolation, and maximize the value of their GPU investments.

With integration of NVIDIA’s MIG technology into OpenShift AI, businesses can unlock the true potential of their GPUs, avoid over-provisioning, and ensure that their AI workloads are running at peak efficiency. This is the future of AI infrastructure: efficient, flexible, and cost-effective.