The future is now: 13 top AI reads from Q2

Artificial intelligence (AI) continues to drive innovation, leading organizations to adopt new strategies for deploying and managing AI solutions. As Q2 comes to an end, we’re reflecting on the invaluable insights shared across the Red Hat community. These are our top AI reads from the past quarter, offering a comprehensive look at how Red Hat is helping organizations manage the complexities of AI, from understanding its true costs and foundational infrastructure to utilizing automation and building intelligent agents. These articles provide practical guidance, strategic perspectives, and key insights into the latest advancements shaping enterprise AI. 

1. The hidden cost of large language models 

Large language models (LLMs) have enormous potential, but their hidden deployment costs are a major challenge. This essential read reveals how strategic model optimization techniques like quantization, pruning, and distillation are key to dramatically reducing costs and boosting performance, helping make enterprise AI more scalable and cost-effective for your organization. 

2. How to navigate LLM model names

Navigating the world of LLM names can feel a bit like decoding a secret language, but it doesn’t have to be overwhelming. This practical guide cuts through the complexity, simplifying everything from branded names and versioning to crucial details like parameter count (think "8B" or "278M") and a model’s specialized purpose (like "instruct," "vision," or "code" models). This guide will help you understand what those names truly mean, making it easier for you to figure out which LLM is best for your specific AI projects and hardware requirements. 

3. The container foundation for tomorrow's AI 

As applications grow more complex and AI transforms every industry, organizations need a strong foundation that can keep pace. This article highlights why Red Hat OpenShift stands out as the comprehensive container platform for tomorrow’s AI workloads. Discover how OpenShift enhances developer productivity, strengthens security, delivers significant ROI, and effortlessly supports your predictive and generative AI (gen AI) initiatives across the hybrid cloud. 

4. Moving AI to the edge: Benefits, challenges and solutions

As AI expands beyond the cloud to address increasingly demanding real-world scenarios, deploying AI at the edge becomes essential for low latency, enhanced privacy, and reliability. This article dives into the benefits and unique challenges of moving AI to the edge, demonstrating how Red Hat’s comprehensive portfolio—including Red Hat OpenShift, Red Hat Device Edge, and Red Hat Ansible Automation Platform—provides the reliable foundation and tools needed to confidently build, deploy, and scale your AI solutions closer to where your data lives. 

5. Introducing Red Hat AI Inference Server: High-performance, optimized LLM serving anywhere

The demand for powerful and efficient gen AI deployments is rapidly growing and Red Hat is meeting it head-on with the introduction of Red Hat AI Inference Server. Find out how this key component of the Red Hat AI platform provides high performance, optimized LLM serving anywhere across your hybrid cloud, thanks to its vLLM core, advanced parallelism, multi-accelerator support, and smooth containerized portability. It’s the flexible, enterprise-grade foundation for accelerating your AI inference workloads. 

6. Llama 4 herd is here with Day 0 inference support in vLLM

Red Hat, working with Meta and UC Berkeley, enabled Day 0 vLLM inference support for the new Llama 4 model family. This integration showcases the value of open source AI collaboration, allowing users to leverage Llama 4 models quickly through vLLM. This article introduces Meta’s latest multimodal models, Llama 4 Scout and Maverick, highlighting their game-changing mixture of experts (MoE) architecture and early fusion capabilities. Read to find out how our collaboration with Meta and contributions to vLLM deliver immediate, high performance inference and optimized deployment, empowering developers to build more efficient, cost-effective and sophisticated AI experiences today. 

7. AI automation: Getting started with Red Hat Ansible Automation Platform

As AI workloads increasingly move from proof-of-concept (POC) to production, IT teams often face mounting pressure to deliver more with increasingly limited resources. This article demonstrates how Red Hat Ansible Automation Platform can help, empowering your teams to automate the deployment, configuration and maintenance of your AI infrastructure, including Red Hat OpenShift AI and Red Hat Enterprise Linux (RHEL) AI. See how automation reduces costs, accelerates time-to-value, enables advanced AIOps workflows, and even uses AI (with Ansible Lightspeed) to streamline your operations for more efficient enterprise AI. 

8. ‌AI automation: How service providers are doing more with less

For service providers navigating the dual challenge of growing revenue and reducing costs, AI automation is proving to be a game-changer. This article explores how deploying AI at scale on an open, cloud-native platform can revolutionize network management, from enhancing RAN optimization and building autonomous intelligent networks to strategically using foundation and small language models. Explore how Red Hat helps service providers consistently automate operations across their entire infrastructure, achieving greater efficiency, agility and a path to doing more with less. 

9. Building enterprise-ready AI agents: Streamlined development with Red Hat AI

AI is evolving, with powerful AI agents now capable of acting autonomously to achieve evolving goals. However, building these agents can be a complex challenge in today’s fragmented AI ecosystem. This article reveals how Red Hat OpenShift AI, featuring the integrated Llama Stack as a unified AI API server, provides the enterprise-ready foundation you need. Discover how this powerful platform simplifies development, standardizes tool interaction and streamlines the entire AI agent lifecycle. 

10. A practical guide to Llama Stack for Node.js developers

Node.js developers, ready to dive into LLMs and AI agents? This guide offers a hands-on exploration of Llama Stack, clarifying how to integrate tool or function calling and agent capabilities into your Node.js applications. From setting up your Llama Stack instance to using the Model Context Protocol (MCP) for tool interaction, you’ll gain the essential insights and code examples needed to build sophisticated LLM-powered solutions. 

11. Models-as-a-Service: Let’s use AI, not just talk about it 

Beyond the strategic discussions, how can enterprises truly operationalize AI at scale efficiently and cost-effectively? This article introduces Models-as-a-Service (MaaS), Red Hat’s practical approach to providing AI models as consumable API endpoints across your enterprise. Learn how adopting MaaS can drive innovation with reduced costs, help accelerate your speed to market, improve privacy and security protections for sensitive data, and empower more users to use AI without becoming AI experts. 

12. 6 benefits of Models-as-a-Service for enterprises

Integrating AI solutions presents significant hurdles for enterprises, from managing complex GPU infrastructure and controlling costs to safeguarding sensitive data. This article introduces MaaS as a revolutionary strategy to overcome these challenges. Discover the 6 core benefits of adopting MaaS, including reduced complexity, lower costs, enhanced security and compliance, faster innovation, greater operational control, and true freedom of choice, all empowering your organization to build a robust and scalable AI ecosystem. 

13. Let AI teach you Linux with Red Hat Enterprise Linux Lightspeed

Ready to unlock deeper Linux enterprise and boost your productivity? This article introduces Red Hat Enterprise Linux Lightspeed, an AI-powered enhancement included with your RHEL subscription. Discover how this intelligent command line assistant provides instant answers and troubleshooting directly from your terminal, while its Insights image builder recommendations simplify the process of creating optimized RHEL images. RHEL Lightspeed puts decades of Red Hat’s Linux knowledge at your fingertips, making you a more efficient and confident RHEL expert. 

Final thoughts

The articles from this past quarter underscore our commitment to delivering open, flexible, and enterprise-ready AI solutions. From strategic cost management and robust infrastructure to advanced automation and intelligent agents, we provide the tools and insights to help your organization confidently build, deploy, and manage AI across the hybrid cloud. Explore these reads to empower your enterprise AI journey.