Red Hat AI

Accelerate the development and deployment of enterprise AI solutions across the hybrid cloud.

Try Red Hat AI Inference Server Try OpenShift AI in the Developer Sandbox
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AI Day of Learning

2 hours | Thursday, Oct. 16 at 10 am EST

Developers, engineers, and practitioners are invited to collaborate with Red Hat’s AI experts in hands-on workshops to explore what’s next for open source AI. Sessions focus on open source AI innovation, vLLM optimization, and agentic AI development.

On Demand
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Deliver AI solutions with Red Hat AI

Red Hat® AI provides the flexibility and consistency you need to deploy and manage predictive and generative AI models for your organization’s workload strategy. The Red Hat AI portfolio includes Red Hat AI Inference Server for optimized inference with vLLM, Red Hat Enterprise Linux® AI for individual Linux server environments, and Red Hat OpenShift® AI for distributed Kubernetes platform environments.

Red Hat AI provides access to small, fit-for-purpose models based on the open source Granite model family that are efficient, cost-effective, and fully supported by Red Hat. It also enables simple yet powerful model tuning, making it easy to align and customize models with your organization’s private data.

Red Hat Enterprise Linux AI

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 Red Hat Enterprise Linux AI

Red Hat OpenShift AI

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 Red Hat OpenShift AI

Red Hat AI Inference Server

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

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 Red Hat AI Inference Server

Red Hat AI use cases

Discover what’s possible with Red Hat AI.

Build, migrate, and run machine learning and predictive AI models

Build machine learning models on your own and leverage advanced AI tooling for delivering predictive models. Red Hat AI also provides support for ITOps teams that want to manage and run models from a Kubernetes-based platform.

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Build, deliver, and run generative AI applications

For gen AI to work effectively for your business, it must be customized to fit your needs and operate on your terms. Get access to a supported, enterprise version of open source tools and technologies for the AI lifecycle.

Gain access to trusted content and security documentation

Safeguard your data with private AI

Red Hat AI provides support for both predictive and generative AI model development and delivery, whether in on-premise data centers or in your own private cloud. Red Hat AI helps reduce the risk of exposing sensitive data by providing support for on-premise and air-gapped deployments.

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Operationalize your AI models

Accelerate your move from experimentation to production with the tools you need to automate the model life cycle. Red Hat AI streamlines model training, validation, storing, and serving by combining MLOps and DevOps capabilities in a single platform.

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Multi-architecture AI deployments

Red Hat AI provides support for multi-cloud, hybrid cloud, and hardware acceleration architectures to ensure high-performance stability and scalability across various infrastructures.

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Red Hat AI: Powered by open source

Red Hat’s product development is rooted in open source and community innovation. Explore the upstream communities that build Red Hat AI.

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vLLM

vLLM, which stands for virtual large language model, is a library of open source code that helps LLMs perform calculations more efficiently and at scale. Specifically, vLLM is an inference server that speeds up the output of gen AI applications by making better use of GPU memory.

Read about vLLM inference
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Llm-d

llm-d is a Kubernetes-native open source framework for distributed large language model (LLM) inference. It supports the complex requirements for LLM inference at scale by prioritizing efficiency.

 

Read about Llm-d
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Granite

Granite is IBM’s third generation of AI language models. Fit for purpose and open source, these enterprise-ready, multimodal models deliver exceptional performance against safety benchmarks and across a wide range of enterprise tasks, from cybersecurity to retrieval-augmented generation (RAG).

Read about Granite models
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Open Data Hub

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.

Contribute to Open Data Hub
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Jupyter

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.

Watch a Jupyter notebook demo
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TensorFlow

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.

Learn about TensorFlow and Quarkus
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PyTorch

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.

Build, train, and run a PyTorch model
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scikit-learn

scikit-learn is a machine learning library for Python. Built on NumPy, SciPy, and Matplotlib, it offers simple and efficient tools for predictive data analysis.

Explore ML with scikit-learn
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Kubeflow

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.

Fine-tune LLMs with Kubeflow

Featured Red Hat AI blogs & articles

Article Featured image for Red Hat OpenShift AI.

Learn how to build a ModelCar container image and deploy it with OpenShift AI.

Article Featured image for Red Hat OpenShift AI.
May 01, 2024

Red Hat OpenShift AI installation and setup

Diego Alvarez Ponce +1

Learn how to install the Red Hat OpenShift AI operator and its components in...

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Learn how to integrate NVIDIA NIM with OpenShift AI to build, deploy, and...

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Discover the new Llama 4 Scout and Llama 4 Maverick models from Meta, with...

Article Featured blog image with the following text: vLLM and DeepSeek
Mar 19, 2025

How we optimized vLLM for DeepSeek-R1

Michael Goin +4

Explore inference performance improvements that help vLLM serve DeepSeek AI...

Article Featured image showing scaffolding that forms the word "V1".

Explore how vLLM's new multimodal AI inference capabilities enhance...

Ready to use Red Hat AI in production?

Take your deployment to the next level. Transitioning to production with Red Hat AI offers you enhanced stability, security, and support. Our dedicated team is here to ensure a smooth migration and to help with any questions you may have.

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