Red Hat AI

Red Hat AI Enterprise is an integrated AI platform for building and running efficient, cost-effective AI models, agentic AI workflows, and AI-powered applications on any infrastructure. 

• Build, fine-tune, and manage workflows for generative, predictive, and agentic AI applications.
• High throughput, low-latency model serving with intelligent scaling.

Learn more

Try It

Deploy your preferred models faster and more cost-effectively across the hybrid cloud with Red Hat AI Inference.

Features and benefits include:

• vLLM runtime maximizes inference throughput and minimizes latency
• Pre-optimized model repository for rapid model serving
• LLM compressor reduces compute costs without sacrificing accuracy

Learn more

Try It

Red Hat OpenShift AI is an MLOps platform that lets you quickly build, train, and deploy AI models and applications across hybrid cloud environments.

Features and benefits include:

• Enterprise MLOps capabilities
• Hardware accelerators and hybrid cloud support for building and delivering AI at scale

Learn more

Try It

Red Hat Enterprise Linux AI is a foundation model platform that helps simplify and accelerate generative AI model development, testing, and deployment within enterprise environments.

Features and benefits include:

• IBM’s Granite family large language models (LLMs)
• Cost-efficient GPU access

Learn more

Try it

Red Hat OpenShift AI is based on the upstream project Open Data Hub, which is a blueprint for building an AI-as-a-Service platform on Red Hat's Kubernetes-based OpenShift Container Platform. Open Data Hub is a meta-project that integrates over 20 open source AI/ML projects into a practical solution.

Project Jupyter, which spun off from the IPython Project in 2014, supports interactive data science and scientific computing across all programming languages. Jupyter is supported by a community of data enthusiasts who believe in the power of open tools and standards for education, research, and data analytics.

TensorFlow is an end-to-end, open source platform for machine learning (ML). Its comprehensive, flexible ecosystem of tools, libraries, and community resources helps developers easily build and deploy ML-powered applications.

PyTorch is an open source machine learning framework that fast-tracks the path from research prototyping to production deployment. It is used for applications such as computer vision and natural language processing.

MLflow is an open-source platform, purpose-built to assist machine learning practitioners and teams in handling the complexities of the machine learning process. MLflow focuses on the full lifecycle for machine learning projects, ensuring that each phase is manageable, traceable, and reproducible.

Kubeflow is an open source framework aimed at simplifying AI/ML workflow deployment at scale. Red Hat OpenShift AI integrates the Kubeflow notebook controller, model serving, and data science pipeline components into the core product.