Beyond the model: Why intelligent infrastructure is the next AI frontier

Beyond the model: Why intelligent infrastructure is the next AI frontier The new challenge: Distributed AI inference A shared vision for a shared problem llm-d: A blueprint for production-grade AI Final thoughts The adaptable enterprise: Why AI readiness is disruption readiness About the author Chris Wright More like this Blog post Blog post Original podcast Original podcast Keep exploring Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share Your large language model (LLM) proof of concept (PoC) was a success. Now what? The jump from a single server to production-grade, distributed AI inference is where most enterprises hit a wall. The infrastructure that got you this far just can't keep up. As discussed in a recent episode of the Technically Speaking podcast , most organizations' AI journey and PoCs begin with deploying a model on a single server—a manageable task. But the next step often requires a massive leap to distributed, production-grade AI inference. This is not simply a matter of adding more machines—we believe this requires a new kind of intelligence within the infrastructure itself—an AI-aware control plane that can help manage the complexity of these unique and dynamic workloads. Deploying LLMs at scale introduces a set of challenges that traditional infrastructure isn't designed to handle. A standard web server, for example, processes uniform requests. In contrast, an AI inference request can be unpredictable and resource-intensive, with variable demands on compute, memory, and networking. Think of it like modern logistics. Moving a small package from one city to another is straightforward. But coordinating a global supply chain requires intelligent logistics management—a system that can track thousands of shipments, dynamically route different types of cargo, and tweak scheduling so everything arrives on time.