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Disclaimer: This personal view on the position of Red Hat within the AI-landscape is based on the personal experience of a specialist solutions architect in application development and AI.
The AI revolution is transforming industries, and enterprises are looking for scalable, secure, and flexible ways to integrate AI into their operations. Red Hat is uniquely positioned to help businesses leverage AI effectively, thanks to our open-source foundation, hybrid cloud capabilities, and enterprise-grade solutions. With the recent acquisition of Neural Magic, we’re taking AI performance to the next level by optimising deep learning models to potentially run efficiently on standard CPUs, reducing hardware dependencies and lowering costs. Neural Magic’s sparse inferencing technology (AI inferencing, similar to an application server for AI models, where trained models make real-time predictions) accelerates AI workloads without the need for GPUs, leveraging software-driven optimisations. Additionally, its compression algorithms significantly reduce model sizes while maintaining accuracy, enabling faster and more efficient AI inferencing across diverse environments, from cloud to edge computing.
AI workloads are complex, requiring robust infrastructure, data pipelines, and security. Red Hat OpenShift, our leading hybrid and multi-cloud application platform, ensures that AI models, applications integrating AI capabilities, and data pipelines can run anywhere: on-premises, in the cloud, or at the edge. This flexibility is crucial for businesses that need to balance performance, cost, and compliance while deploying AI at scale.
AI is not a one-time investment; it’s an iterative process that requires rapid experimentation, deployment, and scaling. OpenShift streamlines AI development, making it easier for teams to integrate AI models into applications while leveraging containerisation and Kubernetes-based orchestration. It enables agile development at the application, data, and AI levels. As an example, if you have a predictive engine for ice cream salons and data scientists need new
APIs integrated (e.g., weather APIs), you don’t want to tell them, "It will be there in the next iteration within half a year." That delay would hinder efficiency and slow down innovation.
Our approach is based on enterprise open source, ensuring innovation without vendor lock-in, which is critical as AI companies currently bear e.g., the electricity costs, but these expenses will soon shift to enterprises. Organisations must be able to adapt quickly, as prices will fluctuate.
Additionally, new AI products are not always released simultaneously across all cloud providers. If a crucial technology is only available in Cloud X or if GPU shortages occur, businesses need the flexibility to migrate seamlessly, and with full community support.
AI introduces new security challenges, from data privacy to model integrity. Red Hat embeds enterprise-grade security across the stack, from the OS to Kubernetes, ensuring compliance with industry regulations. Our commitment to supply chain security, zero-trust principles, and automated security policies helps enterprises deploy AI confidently in sensitive environments.
Gartner: Access rights, data security, is a big concern and a challenge: who can access which data? You can only implement it properly if the underlying infrastructure and components are built with security in mind and when your AI-architecture allows data security.
Additionally, OpenShift provides the capability to easily implement demilitarised zones (DMZs) and service meshes, further enhancing security by segmenting workloads, enforcing policies, and ensuring controlled communication between AI applications and data pipelines.
AI thrives on data, and Red Hat’s middleware portfolio, including Kafka, Camel, Service Mesh, and Serverless, enables real-time, scalable data pipelines. These technologies allow businesses to ingest, process, and serve AI-driven insights efficiently. For example, Kafka’s event-driven architecture supports AI-powered decision-making by enabling continuous data streaming, which is critical for applications like fraud detection and predictive maintenance.
Gartner: Don't forget data management/date pipelines within your AI-story.
Red Hat’s open ecosystem extends AI beyond model training, integrating with databases, big data solutions, and data lakes. Whether you need to connect AI workloads with SAP, IBM, NVIDIA, or Snowflake, our partnerships ensure that enterprises can build a fully integrated AI strategy.
As AI adoption grows, enterprises are increasingly concerned about data control, cost predictability, and adaptability. Local AI models ensure that businesses retain full control over their critical data, mitigating risks even when enterprise contracts are in place. Unlike
cloud-based models that require continuous data input for improvements, local models eliminate concerns about unintended data usage. Moreover, local AI models provide cost predictability, avoiding unexpected spikes in usage-based billing, a common challenge with online models like OpenAI.
Gartner: It's easy to waste money with AI.
Red Hat enables enterprises to manage local AI models via RHEL AI or OpenShift AI, which do not necessarily have to run on-premises but can also be deployed in cloud environments, such as AWS or Azure managed services. This flexibility ensures that businesses can leverage AI while maintaining operational control and cost efficiency.
Gartner: When you understand what’s happening with AI and when you are in control, you can save a tremendous amount of money.
Additionally, InstructLab allows enterprises to fine-tune AI models without requiring data science expertise. This open-source tool empowers domain experts and developers to refine model behaviour efficiently, accelerating AI adoption without the need for specialised ML skills.
With Neural Magic, Red Hat is democratising AI performance by allowing businesses to run deep learning models on commodity hardware. This means enterprises can achieve
high-performance AI without expensive GPUs, significantly reducing costs while maintaining efficiency. This advancement is particularly beneficial for organisations deploying AI in edge computing environments, where specialised hardware is often impractical.
AI is the future of business, and Red Hat ensures it’s enterprise-ready. Whether you are scaling AI across your organisation or exploring new AI-driven use cases, we provide the platform, security, and flexibility to make it happen.
Let’s build the future of AI together.
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