Zero Latency runs Red Hat AI

AI infrastructure is having a very practical moment. The flashy part of the boom was training giant models in giant datacenters. But the harder commercial problem is inference — actually running those models close enough to users, machines, and transactions that delay does not break the product. That is the gap Zero Latency is trying to close, and on May 11 Red Hat said the company has standardized its U.S. network on Red Hat AI Factory with NVIDIA. ### What is Zero Latency actually building? Zero Latency — branded as 0.lat — is not pitching one more centralized cloud region. It is building a distributed inference network, with edge datacenters placed closer to industrial and commercial demand centers across the U.S. Its system, called Zerogrid, aggregates and dispatches AI inference from those decentralized sites instead of hauling every request back to a faraway hyperscale facility. (redhat.com) ### Why does distance matter so much here? Because some AI jobs stop being useful if they arrive late. Industrial automation, real-time transactions, and similar workloads need responses on the order of milliseconds, not the kind of variable delay you can tolerate in a chatbot tab. Red Hat and Zero Latency frame that problem as a “latency tax” — the penalty you pay when compute sits too far from where the data is created and where the action has to happen. (redhat.com) ### So what changed today? The news is not just “Zero Latency uses GPUs.” The company adopted Red Hat AI Factory with NVIDIA as the Kubernetes and AI software foundation for its U.S.-wide network. That bundles Red Hat AI Enterprise with NVIDIA’s stack into something meant to feel less like a pile of custom edge deployments and more like one managed platform. (redhat.com) ### What does Red Hat add beyond basic orchestration? Basically, operating discipline. Red Hat says the platform lets Zero Latency standardize workloads across hundreds of edge sites, do one-click deployment, and manage the whole fleet in a unified way. That matters because edge infrastructure usually gets messy fast — every site turns into its own little snowflake unless you impose the same software, controls, and update path everywhere. (redhat.com) ### And where does NVIDIA fit? NVIDIA is the hardware and AI software anchor underneath the stack. Zero Latency says Zerogrid runs on low-latency nodes built around NVIDIA Blackwell GPUs, while Red Hat AI Factory with NVIDIA is the co-engineered layer that ties Red Hat AI Enterprise to NVIDIA AI Enterprise. In plain English, NVIDIA supplies the accelerated compute and ecosystem; Red Hat tries to make that compute operable at scale. (redhat.com) ### Why call it a “neocloud”? Because the pitch is “cloud behavior without cloud geography.” You still want the things buyers like about cloud — standard APIs, centralized management, repeatable deployment, elastic dispatching of workloads. But you want them on infrastructure that sits physically closer to factories, logistics hubs, and local transaction flows. Turns out that is the real pattern here: private or semi-private infrastructure borrowing the operating model of cloud while rejecting the distance. (redhat.com) ### Is this part of a bigger shift? Yes — and it is a pretty important one. Red Hat’s AI push over the last year has been built around “any model, any accelerator, any cloud,” with AI Inference Server and OpenShift AI aimed at production deployment rather than research demos. Zero Latency is a clean example of where that strategy lands: not in one monolithic AI factory campus, but in a distributed network where inference has to be local, managed, and always on. (redhat.com) ### Bottom line This is really a story about AI growing up. Training still grabs headlines, but inference is where businesses either make the product work or fail. Zero Latency is betting that the winning setup is not cloud versus edge. It is cloud-style software running on edge-heavy infrastructure. (redhat.com 1) (redhat.com 2)