Where AI inference will land: The enterprise IT equation

A distributed center of gravity

Over the next two to three years, the center of gravity for AI inference will become definitively distributed and hybrid. Enterprise boundaries have been loosely defined for a decade, but the advent of pervasive AI will compound this, pushing users, data, workloads, and compute to exist everywhere simultaneously. That will require proactive and pragmatic partitioning of inference tasks.

Small and midsize models (SLMs and MMMs) are already transitioning to run locally on Network Processing Units (NPUs). These models handle daily tasks, such as personal summarization, on-device search, and processing personal context. The rapid development of device-class NPUs ensures that the on-device layer will absorb more of these contextual workflows.

However, the heavier lifts remain a function of the data center. Larger models reliant on extensive retrieval-heavy processes, or complex, collaborative agent workflows, will stay housed in the public cloud or dedicated colocation (colo) GPU clusters. While physical AI and low-latency workloads drive a mandate to perform as much as possible on the device, the core principle remains: do what you can on the device, escalate securely when you must. Multi-tenant agents, long context windows, and heavy multimodal reasoning still demand the superior elasticity and memory bandwidth that current cloud inference infrastructure provides.

Technical and economic gravity of the cloud

Despite the push to the edge, most AI inference today remains anchored in the cloud for specific, unavoidable technical and economic reasons. Any strategy for a hybrid future must first account for these three cloud strengths:

• First is scalable compute and memory. The largest models and the demands of long context require access to High Bandwidth Memory (HBM), high-speed interconnects, and pooled memory architectures. That remains the indisputable strength of major cloud providers and high-end colo facilities. On-device compute cannot yet compete with this pooled, vast capability.
• Second is fleet velocity and control. In the enterprise, rolling out new models, establishing new safety policies, and configuring detailed telemetry must happen in hours, not on the timescale of device refresh cycles. Cloud inference offers clean rollback mechanisms and immediate auditing capabilities across the fleet, providing control and agility critical for enterprise security and governance.
• Third is the underlying unit economics and operational simplicity. Cloud environments offer predictable cost-per-token by abstracting the complexity of hardware management. Cluster-level scheduling, efficient batching, quantization techniques, and right-sizing keep inference costs predictable without standing up GPUs, cooling, or heterogeneous toolchains across every endpoint.

The true edge momentum

The migration of inference to the edge, and eventually the device, is often framed as a conflict between privacy/latency and cost/efficiency. In reality, the driving force is a blend dictated by the specific use case and its regulatory environment.

In real-time or regulated sectors—think robotics in manufacturing, point-of-sale systems in retail, or clinical applications in healthcare—the balance heavily skews toward privacy and latency, often reaching a 70% tilt. Operations in these environments require sub-millisecond response times and mandate data residency to comply with regulations.

However, as enterprise AI fleets scale and NPU proliferation reaches a critical mass, the center of gravity will shift toward cost and efficiency over the coming 24 months. This point is consistent with analyst projections, such as Gartner’s view that 50% of computing will happen at the edge by 2029. As enterprises gain proficiency and expand their AI use cases, the sheer volume of mundane, contextual inference tasks will make offloading them from the central cloud an economic imperative. The network must then support both onramp to cloud and offramp to edge use cases invisibly and safely.

The decisive factor: Policy-driven split inference

The long-term architecture will be distributed, and the mechanism will be split inference. Consumer and enterprise devices will perform a greater set of tasks by default, like wake-word activation, lightweight reasoning, and local file summarization—but they will split the task when local constraints are exceeded. That is likely to occur when tasks require retrieval across multiple accounts, demand multi-agent coordination, or simply exceed the local memory limits.

Academic and industry work on partitioned inference is accelerating, directly mirroring the best practices observed in production networks: push as much compute to the edge as possible, but escalate for heavy lifts. The practical, steady state for the enterprise is policy-driven split inference: local when possible, cloud when beneficial, and deterministic network paths linking the two.

This is why the core IT investment must be in the network fabric. Devices are getting smarter, but successful AI outcomes will still be delivered over the network. That fabric must be:

• Secure: Zero-trust segmentation end-to-end.
• Deterministic: Predictable latency to AIcompute, whether cloud or colo.
• Hyper-agile and Elastic: The policy must follow the workload—whether it lands on a device, in a colo, or in the cloud—without necessitating a network rebuild every time.
• Powered by AI: Getting answers fast to help manage the complexity of this new hybrid compute architecture.

The winners in the AI race are not solely designing a better chip or a bigger model; they are building a simple, secure, and predictable network substrate that enables deterministic paths to AI compute and data, making geographically dispersed, split inference workloads feel local to the end user. This foundation is the strategic mandate for enterprise IT leadership.

About the author

Amir Khan is President, CEO & Founder of Alkira. Alkira is the leader in AI-Native Network Infrastructure-as-a-Service. We unify any environments, sites, and users via an enterprise network built entirely in the cloud. The network is managed using the same controls, policies, and security systems network administrators know, is available as a service, is augmented by AI, and can instantly scale as needed. There is no new hardware to deploy, software to download, or architecture to learn. Alkira’s solution is trusted by Fortune 100 enterprises, leading system integrators, and global managed service providers. 

Article Topics

AI inference  |  AI network fabric  |  AI/ML  |  Alkira  |  edge computing  |  enterprise AI  |  hybrid cloud  |  split inference