Innovation in AI is being driven by Red Hat
In this blog, we will learn how innovation in AI is being driven by Red Hat.
Artificial Intelligence and the Role of Open Source
Artificial intelligence (AI) is increasingly woven into our everyday experiences, creating new possibilities and transforming traditional sectors. Predictive AI, especially through machine learning (ML), has delivered tangible benefits for years, notably in areas like healthcare and fraud prevention. Now, with generative AI (GenAI) entering the mainstream, its transformative potential is even more significant, though still difficult to fully grasp. While innovation headlines dominate the conversation, one critical enabler often goes unmentioned: open source software.
The Evolution of Open Source and Its Enterprise Impact
Once considered a fringe movement led by hobbyists and volunteers, open source software has evolved into a core part of modern enterprise IT. Initially met with hesitation by large organizations, open source gained credibility as collaborative projects like Linux and those under the Apache Software Foundation proved capable of delivering secure, scalable, and cost-effective solutions. Today, it’s difficult to find a software product that doesn’t incorporate open source in some form.
Red Hat has played a key role in promoting and establishing open source technologies as viable solutions for enterprise use. Companies such as Google, IBM, Microsoft, Amazon, NVIDIA, and even digital-native businesses like Netflix and Spotify actively contribute to and benefit from open source projects. Foundational technologies—like the internet, mobile platforms, cloud computing, and now AI—have all grown out of open, collaborative innovation.
The AI ecosystem mirrors this trajectory. Widely used tools and frameworks such as TensorFlow, PyTorch, Kubeflow, and Jupyter Notebook are open source. Much of the software infrastructure for developing and deploying models, both on-premise and in the cloud, is also built on open foundations. The importance of open source in AI is so profound that it often goes unremarked.
Tackling AI’s Energy and Hardware Constraints
As AI models become increasingly sophisticated, they require greater computing power and energy to operate effectively. Shortages of high-end GPUs and specialized chips, along with increasing strain on data center power supplies, pose significant bottlenecks to continued progress.
Open source software provides key advantages in addressing these issues:
- Hardware Agnosticism and Vendor Independence: Open source AI tools support diverse computing environments and architectures, reducing reliance on limited or proprietary hardware and enabling broader compatibility.
- Efficient Resource Scheduling: Technologies like Kubernetes, Kubeflow, and Apache Airflow allow organizations to dynamically manage AI workloads in ways that optimize energy use and resource availability.
- Edge and Federated Learning: Frameworks such as KubeEdge enable decentralized data processing, lowering the demand on central cloud infrastructure and improving efficiency.
To maintain momentum in AI innovation, enterprises must embrace architectures that are both energy-efficient and hardware-flexible—capabilities that open source software can help deliver.
From AI Experimentation to Scalable Adoption
While organizations widely recognize the value AI can bring, identifying an initial use case and building internal expertise remain common hurdles. Starting small with exploratory projects can spark innovation, but scaling to enterprise-grade deployments requires more robust tools and governance.
A complete AI platform can support this evolution, offering security, automation, and scalability. As teams progress from experimentation to production, the right platform helps streamline development, integrate new tools, and automate repetitive processes. It also provides on-demand resource management and supports collaboration across diverse teams.
With the right foundation, organizations can accelerate their AI initiatives while ensuring reliability, security, and operational control.
Red Hat’s Approach to Enterprise AI
Red Hat, known for enterprise-grade open source platforms like Red Hat Enterprise Linux (RHEL), Red Hat OpenShift, and Ansible Automation Platform, has extended its expertise into the AI domain with two key offerings:
Red Hat Enterprise Linux AI (RHEL AI): This solution simplifies the adoption and management of generative AI for businesses, making the technology more approachable and easier to implement at the enterprise level. It includes out-of-the-box support for IBM’s Granite foundation models and simplifies deployment and lifecycle management. RHEL AI supports customization through the InstructLab framework, allowing organizations to fine-tune models using their own data and smoothly scale to more complex environments like OpenShift AI. RHEL AI is fully supported by Red Hat and provides dependable performance across hybrid and multi-cloud deployments.
Red Hat OpenShift AI: Designed to scale with enterprise needs, this platform enables the development and deployment of both predictive and generative AI solutions. It integrates with a wide array of tools and services to enable data scientists to rapidly experiment and deploy models. With support for automation, CI/CD pipelines, and centralized management, OpenShift AI empowers both developers and operations teams to innovate and deliver reliable, AI-powered applications across hybrid environments.
These platforms facilitate collaboration and eliminate silos, driving digital transformation and enhancing competitive edge.
Building the Future of AI with Open Source
Open source has laid the foundation for AI’s rise, and it will continue to be crucial as generative AI becomes more widely adopted. Transparency, collaboration, and governance will be essential as organizations scale their AI capabilities.
Red Hat’s commitment to open source, combined with its enterprise-grade solutions like RHEL AI and OpenShift AI, equips businesses with the tools they need to responsibly harness AI at scale.
Whether you’re beginning your journey with AI model training, looking to scale production workloads, or exploring what’s possible with generative AI, Red Hat provides the platforms and expertise to help unlock AI’s full potential.
