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Techday NZ
21-05-2025
- Business
- Techday NZ
Red Hat launches enterprise AI inference server for hybrid cloud
Red Hat has introduced Red Hat AI Inference Server, an enterprise-grade offering aimed at enabling generative artificial intelligence (AI) inference across hybrid cloud environments. The Red Hat AI Inference Server emerges as an offering that leverages the vLLM community project, initially started by the University of California, Berkeley. Through Red Hat's integration of Neural Magic technologies, the solution aims to deliver higher speed, improved efficiency with a range of AI accelerators, and reduced operational costs. The platform is designed to allow organisations to run generative AI models on any AI accelerator within any cloud infrastructure. The solution can be deployed as a standalone containerised offering or as part of Red Hat Enterprise Linux AI (RHEL AI) and Red Hat OpenShift AI. Red Hat says this approach is intended to empower enterprises to deploy and scale generative AI in production with increased confidence. Joe Fernandes, Vice President and General Manager for Red Hat's AI Business Unit, commented on the launch: "Inference is where the real promise of gen AI is delivered, where user interactions are met with fast, accurate responses delivered by a given model, but it must be delivered in an effective and cost-efficient way. Red Hat AI Inference Server is intended to meet the demand for high-performing, responsive inference at scale while keeping resource demands low, providing a common inference layer that supports any model, running on any accelerator in any environment." The inference phase in AI refers to the process where pre-trained models are used to generate outputs, a stage which can be a significant inhibitor to performance and cost efficiency if not managed appropriately. The increasing complexity and scale of generative AI models have highlighted the need for robust inference solutions capable of handling production deployments across diverse infrastructures. The Red Hat AI Inference Server builds on the technology foundation established by the vLLM project. vLLM is known for high-throughput AI inference, ability to handle large input context, acceleration over multiple GPUs, and continuous batching to enhance deployment versatility. Additionally, vLLM extends support to a broad range of publicly available models, including DeepSeek, Google's Gemma, Llama, Llama Nemotron, Mistral, and Phi, among others. Its integration with leading models and enterprise-grade reasoning capabilities places it as a candidate for a standard in AI inference innovation. The packaged enterprise offering delivers a supported and hardened distribution of vLLM, with several additional tools. These include intelligent large language model (LLM) compression utilities to reduce AI model sizes while preserving or enhancing accuracy, and an optimised model repository hosted under Red Hat AI on Hugging Face. This repository enables instant access to validated and optimised AI models tailored for inference, designed to help improve efficiency by two to four times without the need to compromise on the accuracy of results. Red Hat also provides enterprise support, drawing upon expertise in bringing community-developed technologies into production. For expanded deployment options, the Red Hat AI Inference Server can be run on non-Red Hat Linux and Kubernetes platforms in line with the company's third-party support policy. The company's stated vision is to enable a universal inference platform that can accommodate any model, run on any accelerator, and be deployed in any cloud environment. Red Hat sees the success of generative AI relying on the adoption of such standardised inference solutions to ensure consistent user experiences without increasing costs. Ramine Roane, Corporate Vice President of AI Product Management at AMD, said: "In collaboration with Red Hat, AMD delivers out-of-the-box solutions to drive efficient generative AI in the enterprise. Red Hat AI Inference Server enabled on AMD InstinctTM GPUs equips organizations with enterprise-grade, community-driven AI inference capabilities backed by fully validated hardware accelerators." Jeremy Foster, Senior Vice President and General Manager at Cisco, commented on the joint opportunities provided by the offering: "AI workloads need speed, consistency, and flexibility, which is exactly what the Red Hat AI Inference Server is designed to deliver. This innovation offers Cisco and Red Hat opportunities to continue to collaborate on new ways to make AI deployments more accessible, efficient and scalable—helping organizations prepare for what's next." Intel's Bill Pearson, Vice President of Data Center & AI Software Solutions and Ecosystem, said: "Intel is excited to collaborate with Red Hat to enable Red Hat AI Inference Server on Intel Gaudi accelerators. This integration will provide our customers with an optimized solution to streamline and scale AI inference, delivering advanced performance and efficiency for a wide range of enterprise AI applications." John Fanelli, Vice President of Enterprise Software at NVIDIA, added: "High-performance inference enables models and AI agents not just to answer, but to reason and adapt in real time. With open, full-stack NVIDIA accelerated computing and Red Hat AI Inference Server, developers can run efficient reasoning at scale across hybrid clouds, and deploy with confidence using Red Hat Inference Server with the new NVIDIA Enterprise AI validated design." Red Hat has stated its intent to further build upon the vLLM community as well as drive development of distributed inference technologies such as llm-d, aiming to establish vLLM as an open standard for inference in hybrid cloud environments.
Forbes
01-04-2025
- Business
- Forbes
IBM's Enterprise AI Strategy: Trust, Scale, And Results
I Watsonx AI IBM generative AI platform displayed on a smartphone. On 10 August 2023 in Brussels, ... More Belgium. (Photo illustration by Jonathan Raa/NurPhoto via Getty Images) BM has rapidly established itself as a serious enterprise AI contender. It combines a full-stack platform strategy, proprietary models, deep integration with Red Hat hybrid cloud infrastructure, and global consulting scale. It's executing a multi-pronged approach that is already delivering operational leverage and financial upside. Its approach is paying off. In its most recent earnings, IBM disclosed that it'd grown its book of AI-related business to $5 billion in less than two years, with approximately 80% of that stemming from consulting engagements and the remainder from software subscriptions. IBM detailed its AI strategy at its recent investor day. It's a strategy centered on a pragmatic, enterprise-first approach that can deliver trusted, efficient, and domain-relevant AI solutions. IBM's AI straetgy brings together infrastructure software from Red Hat, foundation models from IBM Research, customer enablement capabilities from IBM Consulting, and integration with a broad ecosystem of partners. Unlike some competitors focused on developing massive general-purpose models, IBM's bet is on smaller, specialized models, deployed across hybrid cloud environments, and tightly integrated with its consulting services and data platforms. The goal is to help businesses operationalize AI in a way that's scalable, secure, and aligned with real-world enterprise needs. This is an approach particularly well-suited for companies in regulated industries — such as financial services, healthcare, and government — where data security, governance, and compliance concerns are paramount. At the core of IBM's AI stack is watsonx, an end-to-end platform designed to support the entire AI lifecycle. Watsonx allows businesses to build and train models using both IBM's proprietary tools and open-source models while also enabling them to fine-tune those models using their proprietary data. One of the most critical components of this platform is Granite, IBM's family of smaller, purpose-built foundation models tailored for enterprise use cases like code generation, document processing, and virtual agents. These cost-efficient, interpretable models are built to perform well in sensitive, highly regulated environments. IBM has even open-sourced several Granite models to support transparency and community-led development. IBM's AI technology is further strengthened by its integration with Red Hat's hybrid cloud tools. OpenShift AI and RHEL AI provide the infrastructure to build, deploy, and manage AI applications across on-premises, private, and public cloud environments. This hybrid model offers flexibility for enterprises that need control over their data while still wanting the agility of cloud-native services. Global system integrators are integral to helping IT organizations navigate complex new technologies, especially enterprise AI. Enterprises often struggle to understand the new technology while also attempting to extract value quickly. GSIs thrive in this market, promising quick time-to-value for AI transformation projects. A defining strength of IBM's approach is the synergy between its AI stack and its global consulting business. IBM Consulting, with its 'hybrid by design' approach, is central in driving client adoption of watsonx and Granite. This helps enterprises bring AI into mission-critical workflows across HR, procurement, customer service, and supply chain operations. IBM Consulting competes directly against companies like NTT DATA, Deloitte, Cognizant, and Capgemini. Each of these companies has AI platforms in place and AI-specific engagement models that offer a compelling choice for enterprises. Partnerships play a critical role in IBM's AI strategy. The company has built a rich ecosystem of collaborators that includes hyperscalers, chipmakers, open-source communities, and enterprise software vendors. Rather than trying to build and control every component internally, IBM focuses on integrating and orchestrating AI capabilities across a broad range of technologies. This strategy enables IBM to deliver value through its innovations and the strength of its partner network. A example of this is IBM's integration of watsonx with platforms like SAP, Salesforce, and ServiceNow. Operating within familiar business applications allowsd customers to leverage IBM's AI without disrupting existing workflows. T Collaboration extends to the systems integrators and hardware vendors that form the backbone of many enterprise deployments. IBM is working alongside companies like Dell, Lenovo, and Nokia to deliver AI-ready infrastructure, and has formed go-to-market alliances with integrators and resellers to accelerate customer adoption. Financially, IBM's AI bets are translating into real momentum. In its latest earnings release, the company reported that its book of AI business has grown to over $5 billion, and its software division posted double-digit growth in 2024 — its strongest in years — mainly fueled by demand for AI and hybrid cloud solutions. Free cash flow climbed to $12.7 billion, and IBM reports that for every dollar spent on watsonx, clients invest five to six dollars more across IBM's broader software and consulting portfolio. This multiplier effect highlights the strength of IBM's integrated offerings. Most AI-related revenue still comes from consulting, reflecting the power of IBM's services-led go-to-market model. However, the company's strategy of combining Red Hat infrastructure, watsonx software, and consulting expertise is clearly gaining traction. The tight integration of its software, infrastructure, and services sets IBM apart in the enterprise AI space. Red Hat's OpenShift and RHEL AI form the infrastructure foundation of IBM's AI strategy, powering the deployment of watsonx across diverse cloud and edge environments. IBM Consulting brings the human element, delivering AI solutions tailored to industry-specific challenges in sectors such as banking, healthcare, manufacturing, and government. Together, these arms of IBM provide the technological muscle and domain expertise needed to bring AI from concept to production at enterprise scale. IBM's end-to-end approach, spanning model development, deployment, governance, and business transformation, is a strategy that's clearly working. It's also a strategy that's difficult for competitors to match. As bookings grow, platform adoption accelerates, and ecosystem partnerships deepen, IBM is reshaping its identity around AI, hybrid cloud, and consulting. The company's ability to commercialize AI through a tightly connected stack of products, platforms, and people makes it one of the most interesting and credible enterprise AI players today. Disclosure: Steve McDowell is an industry analyst, and NAND Research is an industry analyst firm, that engages in, or has engaged in, research, analysis and advisory services with many technology companies; the author has provided paid services to many of the companies named in this article in the past and may again in the future, including IBM. Mr. McDowell does not hold any equity positions with any company mentioned.
Mid East Info
20-02-2025
- Business
- Mid East Info
AI Freedom of Choice with Red Hat - Middle East Business News and Information
Red Hat collaborates with NVIDIA, Lenovo, Microsoft, AWS, Dell, AMD and Intel Red Hat continues to build upon its long-standing collaborations with major IT players to help customers implement and extend AI innovation across hybrid cloud environments. Red Hat Enterprise Linux AI (RHEL AI) and Red Hat OpenShift AI now offer a supported, optimised experience with a wide range of GPU-enabled hardware and software offerings from NVIDIA, Lenovo, Dell, Microsoft, AWS, Intel and others. Artificial intelligence (AI) model training requires optimized hardware and powerful computation capabilities. AI platforms must also support a broad choice of accelerated compute architectures and GPUs. Customers can get more from RHEL AI and Red Hat OpenShift AI by extending it with other integrated services and products announced in the last eight months: RHEL AI on Lenovo ThinkSystem SR675 V3 servers RHEL AI on Dell PowerEdge RHEL AI support for AMD Instinct Accelerators RHEL AI on Microsoft Azure RHEL AI and Red Hat OpenShift AI on AWS Red Hat OpenShift AI with Intel Gaudi AI and Intel Xeon and Core processors Red Hat OpenShift with NVIDIA AI Enterprise The Cloud is Hybrid. So is AI. For 30 years, open source has been a driving force behind innovation. Red Hat has played a key role in this evolution, first with enterprise-grade Linux (RHEL) in the 2000s and later with Red Hat OpenShift for containers and Kubernetes. Today, Red Hat continues this journey with AI in the hybrid cloud. AI models often need to run as close to an organization's data as possible to reduce latency and improve efficiency. This requires models to be supported wherever needed, from the datacenter to public clouds to the edge. AI platforms must be able to stretch across all of these footprints, seamlessly and without integration challenges. AI is the ultimate hybrid workload.
Zawya
20-02-2025
- Business
- Zawya
AI freedom of choice with Red Hat
Red Hat continues to build upon its long-standing collaborations with major IT players to help customers implement and extend AI innovation across hybrid cloud environments. Red Hat Enterprise Linux AI (RHEL AI) and Red Hat OpenShift AI now offer a supported, optimised experience with a wide range of GPU-enabled hardware and software offerings from NVIDIA, Lenovo, Dell, Microsoft, AWS, Intel and others. Artificial intelligence (AI) model training requires optimized hardware and powerful computation capabilities. AI platforms must also support a broad choice of accelerated compute architectures and GPUs. Customers can get more from RHEL AI and Red Hat OpenShift AI by extending it with other integrated services and products announced in the last eight months: RHEL AI on Lenovo ThinkSystem SR675 V3 servers RHEL AI on Dell PowerEdge RHEL AI support for AMD Instinct Accelerators RHEL AI on Microsoft Azure RHEL AI and Red Hat OpenShift AI on AWS Red Hat OpenShift AI with Intel Gaudi AI and Intel Xeon and Core processors Red Hat OpenShift with NVIDIA AI Enterprise The Cloud is Hybrid. So is AI. For 30 years, open source has been a driving force behind innovation. Red Hat has played a key role in this evolution, first with enterprise-grade Linux (RHEL) in the 2000s and later with Red Hat OpenShift for containers and Kubernetes. Today, Red Hat continues this journey with AI in the hybrid cloud. AI models often need to run as close to an organization's data as possible to reduce latency and improve efficiency. This requires models to be supported wherever needed, from the datacenter to public clouds to the edge. AI platforms must be able to stretch across all of these footprints, seamlessly and without integration challenges. AI is the ultimate hybrid workload. For further information, please contact: Orient Planet Group (OPG) Email: media@ Website:
TECHx
11-02-2025
- Business
- TECHx
Red Hat's Approach to AI and Hybrid Cloud
Red Hat's Approach to AI and Hybrid Cloud: Exclusive Insights from the VP In an exclusive interview with Martin Lentle, Vice President of Northern Europe, Middle East & Africa at Red Hat, we explore the strategies behind Red Hat's impressive growth, the challenges businesses face in AI adoption, and the company's future vision for the hybrid cloud landscape. Driving Red Hat's Growth Through Open-Source and Hybrid Cloud Red Hat's success can be attributed to its unwavering focus on open-source technologies that empower businesses to integrate artificial intelligence (AI) into their operations. Martin explains how Red Hat has taken a strategic approach by developing platforms, such as Red Hat Enterprise Linux AI (RHEL AI) and Red Hat OpenShift AI, to address the growing demand for AI and hybrid cloud solutions. Unlike traditional proprietary AI models, Red Hat focuses on providing businesses with the flexibility to develop, test, and deploy AI models seamlessly across various infrastructures, including on-premises, public clouds, and edge environments. This approach not only reduces latency but enhances operational efficiency by running AI models closer to data sources. 'We believe in offering businesses a framework that helps them scale AI implementations with ease, without requiring specialized expertise,' Martin notes. 'Our solutions are designed to run AI models across any infrastructure, ensuring that AI adoption becomes accessible and cost-effective for businesses, regardless of size.' In this way, Red Hat is shifting away from complex, monolithic AI models and instead promoting smaller, optimized models that can be customized to specific business needs. By empowering businesses to build and fine-tune these models in-house, Red Hat addresses the complexity and cost associated with large-scale AI projects. Challenges in AI Integration: Flexibility is Key When discussing the hurdles businesses encounter with AI adoption, Martin highlights the integration of AI into traditional infrastructures as one of the major challenges. AI doesn't replace existing applications; it complements and enhances them. This creates the need for advanced solutions that ensure AI models can seamlessly work alongside traditional applications. Red Hat's hybrid cloud solutions play a crucial role in this process by offering businesses the flexibility to integrate AI models into their current systems. 'Businesses are increasingly adopting a diverse array of smaller, specialized AI models instead of relying on a single, universal model for all their needs,' Martin explains. 'Our tools, such as OpenShift AI, empower companies to develop, train, and deploy multiple models across different environments, all without the need for deep, specialized skills.' The flexibility of Red Hat's hybrid cloud solutions ensures AI workloads can be deployed across a variety of infrastructures—whether on-premises, in the cloud, or at the edge—enabling businesses to reduce latency and enhance efficiency in their AI operations. AI Everything Global: A Platform to Showcase Innovation The AI Everything Global event in Dubai offers a valuable opportunity for Red Hat to showcase its contributions to the AI landscape. Martin views the event as an essential platform to demonstrate how Red Hat's open-source AI platforms can drive AI adoption and innovation across hybrid cloud environments. As businesses move towards more distributed AI models, Red Hat is leading the charge in offering scalable solutions that bridge the gap between traditional and modern AI applications. 'The AI Everything Global event allows us to showcase how our solutions enable businesses to build and run AI models in-house, making AI more accessible and manageable,' he says. 'Our hybrid cloud solutions provide the agility required to manage AI workloads efficiently, especially as AI becomes more decentralized and positioned closer to data sources.' With an increasing emphasis on smaller, optimized AI models, Red Hat's approach aligns with industry trends that emphasize flexibility and customization. By facilitating AI adoption through open-source platforms and hybrid cloud technologies, Red Hat ensures that businesses can implement AI solutions that are not only effective but also cost-efficient. As Red Hat continues to lead the way in hybrid cloud AI solutions, its participation in the AI Everything Global event solidifies its position as a driving force in the AI revolution, helping enterprises build future-proof, scalable AI infrastructures. In conclusion, Martin Lentle's insights underscore Red Hat's commitment to simplifying AI integration and ensuring businesses can harness the power of AI without being overwhelmed by complexity. Through its innovative solutions, Red Hat is shaping the future of AI and hybrid cloud, making it a key player in the ongoing digital transformation.
