Why Red Hat is Flaunting its AI Feathers
Open-source enterprise solutions rely on regular updates, driving faster innovation, cost efficiency, and freedom from vendor lock-ins, all backed by a global community of contributors. Red Hat, a leading provider of enterprise open-source solutions, is leveraging this strength to take the lead in the AI space. At the recent Red Hat Summit 2025 in Boston, the company announced its newly acquired capabilities to scale open, flexible AI. It said it has integrated Meta’s Llama 4 and the vLLM inference engine into its AI platform, and introduced llm-d for efficient, container-native AI workflows. These tools, paired with hybrid cloud capabilities, aim to democratise AI across various environments—on-premises, cloud, and edge.Introducing its capabilities, Red Hat has stated that it’s expanding its AI portfolio with a single mission in mind: to make AI usable, flexible, and production-ready for enterprises, without sacrificing choice, openness, or scalability. Speaking at the briefing held in the heart of Bengaluru, Ameeta Roy, senior director (Solution Architecture) at Red Hat, laid out how the company is bridging the gap between upstream AI innovation (in the open-source world) and enterprise-grade deployment. “Earlier, we used to say–any app, any cloud, anywhere. Now, we’ve expanded that to include any AI model, any accelerator, any cloud,” Roy emphasised, noting that this gives enterprises the flexibility to build and run AI the way they need using open standards and supported infrastructure.” The company further stated that its journey into AI is deeply rooted in the explosion of generative AI that followed the release of ChatGPT in late 2022. While earlier enterprise AI focused on predictive analytics, fraud detection, forecasting, and similar applications, generative AI has reset expectations. “Now every enterprise is asking how they can leverage AI, but few can afford to build or train large models on their own,” Roy observed. “The real challenge lies not in training, but in inferencing—putting models into production.”That’s where, she said, Red Hat’s AI offerings step in. An Acquisition That Upped the Game Through its Red Hat Inference Server, the company offers enterprise-grade inferencing powered by the open-source vLLM project from UC Berkeley. Red Hat acquired Neural Magic, one of vLLM’s top contributors and is now integrating its memory-optimised inferencing capabilities into its platform, she announced. Roy explained that enormous amounts of compute are involved in generating each token. He added that by applying techniques such as quantisation, compression, and caching, customers can reduce costs and latency without sacrificing accuracy. Red Hat, she shared, is also tackling the need for scalable inferencing across hybrid environments by co-founding llm-d, a Kubernetes-native framework to orchestrate inference workloads across infrastructure. Besides Kubernetes architecture, llm-d is powered by vLLM-based distributed inference and intelligent AI-aware network routing. “Kubernetes revolutionised microservices. We’re now using the same logic for inference at scale,” Roy said. The project is being supported by Red Hat, IBM Research, Google, Nvidia, and others, reflecting a growing industry consensus around open AI infrastructure standards. Further reinforcing its agentic AI vision, Red Hat is working closely with Meta’s Llama Stack and backing the Model Context Protocol (MCP) standard, which facilitates model-to-model coordination in complex multi-agent tasks. “Agentic AI requires models to collaborate. MCP provides a standard for how that interaction happens,” Roy noted. Red Hat plans to bring Llama Stack as a standard AI application framework to its broader AI portfolio. Crucially, she said, Red Hat isn’t limiting these capabilities to its own ecosystem. “We’re making sure Red Hat AI inference server works on other Linux distributions and Kubernetes variants,” Roy confirmed. “We’re committed to openness—this isn’t about lock-in.” The company’s inference stack supports a wide array of hardware accelerators, including Nvidia, AMD (Instinct GPUs), Intel, Google TPUs, and IBM’s AIU, with several already in general availability and others in preview. “Some of these will have out-of-box support,” Roy said, pointing to AMD’s GPU collaboration and Google Cloud’s Gemma model support as examples of Red Hat’s ecosystem-first strategy. Training own models Meanwhile, for customers who want to train their own models, the company claims to build tools like InstructLab, which enables domain experts to generate question-answer pairs and synthetic data to fine-tune models. It also supports retrieval augmented generation (RAG) workflows, enabling enterprises to augment model outputs with live data sources, such as updated documentation. Roy explained that even if the model hasn’t been trained on the latest release, RAG can assist the model in pulling that knowledge in real time. Hybrid, edge commitment But Red Hat isn’t just focused on cloud-scale AI. Its commitment to hybrid and edge computing remains central. With Red Hat Enterprise Linux AI and Red Hat OpenShift AI, the company offers foundational AI infrastructure across all deployment environments. Security, a key concern in enterprise AI adoption, is another area of focus. Roy highlighted the looming threat of quantum computing on current cryptographic systems. On the virtualisation front, Red Hat is enabling organisations to migrate from traditional VM (virtual machine) environments to container-native infrastructures, she shared. It offers both full-scale OpenShift Virtualisation and a lightweight virtualisation engine for those not yet ready to adopt containers. “We’re seeing 3x growth in customers, 2x the number of production clusters, and a massive increase in VMs managed under OpenShift,” Roy shared, citing a major Indian telco that consolidated VM and container environments using Red Hat’s tools. The company has built out migration assessment tools, factory-mode migration automation, simplified dashboards, and enhanced ecosystem support to make modernisation seamless. “We know customers can’t flip a switch. So we help them move gradually—from legacy to cloud-native, from VM to container, from isolated tools to integrated platforms,” she said. Roy emphasised Red Hat’s long-standing commitment to open source. “We don’t just consume community innovation. We contribute, stabilise, secure, and make it enterprise-ready,” she said. “When the world converges around common open standards, like vLLM, LLMT, and MCP, that’s when innovation becomes accessible, repeatable, and scalable.”
The company is scaling open, flexible AI with vLLM, llm-d, and hybrid tools to democratise enterprise AI across environments.
The company is scaling open, flexible AI with vLLM, llm-d, and hybrid tools to democratise enterprise AI across environments.
["AI", "enterprise AI adoption", "Open Source", "red hat"]
["ChatGPT", "agentic AI", "red hat", "Open Source", "AI", "Llama 4", "ML", "Ray", "Aim", "analytics", "generative AI", "enterprise AI adoption", "retrieval augmented generation"]
["AI", "ML", "analytics", "generative AI", "agentic AI", "ChatGPT", "Llama 4", "Aim", "Ray", "retrieval augmented generation"]
Bala is a journalist covering Indian tech companies and startups from Bengaluru.