Inference connectivity

Building the network foundation for distributed intelligence

Inference connectivity

The next generation of AI inference services

Distributed inferencing is the next frontier for AI. Successful buildout of distributed inferencing will rely increasingly on close partnerships between AI and cloud providers and telecommunication providers. AI and cloud providers bring accelerated compute and AI expertise while telecommunication providers bring trusted connectivity services, distributed sites and customer relationships.

Together, they can create a scalable AI platform to deliver business and consumer AI services, unlocking new revenue potential and market reach.

Turning distributed compute into connected intelligence

As AI becomes more distributed, the network becomes a critical part of the service and a key platform for monetization.

Agentic systems and physical AI will require models to run everywhere, continuously and at scale. Workloads may move dynamically among enterprises, neoclouds, hyperscalers and service providers, often crossing organizational or geographic boundaries. At the same time, proximity will be a defining element of the service design for many emerging applications.

This creates an opportunity for open commercial models that support multiple providers and multi-cloud environments while delivering a trusted, consistent service.

Solution note

Inference connectivity - A market opportunity created by distributed AI inference

The growing scale of AI inference

60%

of all AI demand will come from inference by 2030

(source: McKinsey)

120 Q

tokens per month by 2030, representing an 89% CAGR in the token economy

(source: Goldman Sachs)

117 GW

of computing capacity will be consumed by inference by 2030

(source: McKinsey)

What capabilities matter for distributed inference connectivity?

The network for distributed inference connectivity must do more than provide bandwidth between locations. It needs a combination of scale, predictable performance, resilience, security and automation to support dynamic AI workloads across clusters, clouds and administrative domains.

Scalable and efficient

Support growing traffic between AI clusters, data platforms, caches, applications and users with high-capacity IP and optical networking. Expand capacity economically while improving bandwidth utilization, power efficiency and cost per bit.

Deterministic performance

Use traffic engineering, latency-aware paths, hierarchical quality of service, network slicing and real-time telemetry to deliver predictable application performance and measurable service levels.

Resilient and available

Protect active inference sessions and workflows with geodiverse paths, fast restoration, redundant service endpoints and closed-loop assurance. Maintain continuity through network failures, site outages and traffic shifts.

Secure and isolated

Extend private connectivity and segmentation from customer sites to inference resources. Combine tenant isolation, encryption, DDoS protection and trusted infrastructure with policy-based path control to support security, privacy and sovereignty requirements.

Automated across clouds and domains

Use open APIs, model-driven management and standards-based handoffs to coordinate connectivity across telecommunications providers, neoclouds, hyperscalers and enterprises. Provision persistent or on-demand services and adapt them as workload placement changes.

High-performance IP routing

Our platforms are built to address evolving AI demands, focusing on scalability, reliability and performance.

Explore IP routing

Scalable, Intelligent optical networking

Our optical innovations and technologies deliver greater scale, network simplicity and robust security for the AI era.

Explore optical networks

Multilayered network security

Our approach enables you to build scalable, secure and trusted networks that protect your AI ambitions.

Explore IP networks security

Why Nokia?

We combine decades of extensive expertise in industry-leading IP routing and optical innovations, advanced security technologies, automation platforms, mobile and fixed networks and open network architectures. Oco-innovating with customers, partners and the wider industry ecosystem to design and build trusted solutions for connecting distributed AI infrastructure.

Frequently asked questions

What is distributed inferencing?

Distributed inferencing means that AI inference capacity is geographically distributed and the AI model’s inference workload is directed to the most appropriate compute location instead of relying only on centralized data centers.

What is inference connectivity?

Inference connectivity delivers scalable, efficient and deterministic connectivity for distributed AI inference clusters so they can operate as a single federated system.

How can telecommunication providers monetize distributed inferencing?

Telecommunication providers can monetize distributed inferencing by combining physical sites and wide-area networks into differentiated AI connectivity services towards AI and cloud providers.

How can AI and cloud providers deploy distributed inference services?

AI and cloud providers can partner with telecommunication providers to deploy inference capacity in locations that meet their operational and economic needs. These partnerships combine distributed sites with private connectivity and predictable service levels, while enabling AI and cloud providers to maintain visibility and control across locations.