Summit 2025

Discover the comprehensive security and scalability measures for a Models-as-a-Service (MaaS) platform in an enterprise environment.

Learn how to build a Model-as-a-Service platform with this simple demo. (Part 3 of 4)

Explore the architecture of a Models-as-a-Service (MaaS) platform and how enterprises can create a secure and scalable environment for AI models. (Part 2 of 4)

Explore Red Hat Summit 2025 with Dan Russo and Repo, the Red Hat Developer mascot!

Discover how IBM used OpenShift AI to maximize GPU efficiency on its internal AI supercomputer, using open source tools like Kueue for efficient AI workloads.

llm-d delivers Kubernetes-native distributed inference with advanced optimizations, reducing latency and maximizing throughput.

LLM Semantic Router uses semantic understanding and caching to boost performance, cut costs, and enable efficient inference with llm-d.

Discover the features and benefits of Red Hat Advanced Developer Suite, including enhanced security.

Optimize model inference and reduce costs with model compression techniques like quantization and pruning with LLM Compressor on Red Hat OpenShift AI.

Build and run RHEL applications on Windows using the Windows Subsystem for Linux. Download pre-built RHEL images or create your own customized images.

Learn how to use synthetic data generation (SDG) and fine-tuning in Red Hat AI to customize reasoning models for your enterprise workflows.

Discover a simple way to provision a bootc system with the new system-reinstall-bootc tool and install a bootc image from within an existing package mode system.

Learn how to run a fraud detection AI model using confidential virtual machines on RHEL running in the Microsoft Azure public cloud.