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As AI shifts from model training toward widespread inference, compute placement, orchestration and programmable connectivity are emerging as critical AI infrastructure challenges
The first phase of the AI infrastructure boom has been relatively easy to describe: build bigger as quickly as possible.
Hyperscalers and infrastructure providers have poured capital into increasingly large AI factories, connecting thousands of accelerators with high-performance scale-up and scale-out networks. That investment cycle is far from over. But the next phase of AI infrastructure introduces a different problem that cannot be solved simply by adding more GPUs to ever-larger clusters.
As AI moves from model development toward widespread consumption, inference changes the architectural equation.
Inference workloads are unpredictable and highly variable. A large reasoning model may benefit from centralized infrastructure where requests can be batched and accelerators kept highly utilized. A real-time video application, industrial robot or sovereign AI service may instead require compute closer to the user, machine or data source. Latency, cost, privacy, data sovereignty, energy availability and application quality can all influence the answer to a deceptively simple question: Where should this workload run?
That question is at the center of the new RCRTech report, Building AI-era network fabrics: Architecting for distributed inference at scale.
The report examines the transition from conventional scale-up, scale-out and scale-across architectures toward what Nokia describes as multi-dimensional scaling, which connects independent pools of compute across AI factories, regional clouds, enterprise environments, telecom infrastructure and the edge. In this model, the network becomes more than the mechanism that transports AI traffic. It becomes an active part of the AI system, providing the programmability, telemetry and deterministic performance needed to make dynamic workload placement possible.
In the push toward what NVIDIA and others have described as an AI gird, connectivity is only part of the problem.
Distributed inference also puts new pressure on the orchestration layer. Traditional application schedulers were not designed to make decisions based on GPU memory, model state, KV caches, accelerator type, power constraints and data-governance policies simultaneously. Red Hat’s contribution to the report explores how an inference-aware operational layer can turn heterogeneous infrastructure into a more consistent, governed AI platform.
CoreWeave adds another dimension: distribution should not become an objective in itself. The correct architecture depends on the workload, and infrastructure abstraction has to strike a balance between hiding operational complexity and preserving the controls that affect performance, model quality and economics. Ultimately, the relevant metric may be less about raw infrastructure efficiency and more about the amount of useful work an application produces for every dollar spent.
The report also examines what distributed AI could mean for telecom operators whose networks, facilities and geographic footprints position them close to where inference will increasingly be consumed. But geography alone will not create an AI business. Operators will need programmable infrastructure, new commercial models and tighter integration with AI cloud and orchestration ecosystems.
The AI factory solved one problem by concentrating enough compute to build and run increasingly capable models. The next challenge is coordinating that intelligence across many places.
Download the full report, Building AI-era network fabrics, to explore how networking, orchestration and distributed compute are converging into the infrastructure architecture for the inference era.
