Inference connectivity
Building the network foundation for distributed intelligence
Distributed inference needs more than distributed compute
Distributed inferencing is the next frontier for AI. Driven by agentic and physical AI, inference compute resources will require deployment closer to end-users to meet performance, cost and sovereignty requirements for many AI applications. Due to power constraints, construction costs, and regulations, as these resources get closer to end users, they will deployed in smaller more highly distributed clusters.
Successful buildout of distributed inferencing will rely increasingly on close partnerships between AI and cloud providers, telecommunication providers and connectivity solution providers:
- AI and cloud providers bring accelerated compute and AI expertise.
- Telecommunication providers bring trusted connectivity services, distributed sites and customer relationships.
- Connectivity solution providers bring the IP and optical networking solutions enable the scalability, security, visibility, and deterministic performance necessary to meet the application demands.
Together, they can create a scalable AI platform to deliver business and consumer AI services, unlocking new revenue potential and market reach.
Inference is becoming the dominant AI workload
of all deployed AI resources will be inference optimized by 2030
(source: McKinsey)
quadrillion tokens per month forecast by 2030
(source: Goldman Sachs)
of inference computing capacity estimated by 2030
(source: McKinsey)
Turning distributed compute into connected intelligence
Agentic and physical AI can push inference closer to users, enterprises, devices and data sources. The right execution point may change with the application, available capacity, data location, cost, latency or policy requirement.
These workloads can span enterprise sites, neoclouds, hyperscalers, service providers and data centers, often crossing geographic and organizational boundaries. Basic reachability connects the locations. An inference service also needs the network to help maintain performance, availability, isolation and visibility as traffic patterns and workload placement change.
Five key capabilities for inference connectivity
Inference connectivity solutions need to support service levels across locations, clouds and providers. Five network capabilities form the foundation.
Efficient scalability
Carry growing traffic between AI clusters, data platforms, caches, applications and users with high-capacity IP and optical networking. Improve bandwidth utilization, power efficiency and cost per bit as demand grows.
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.
Programmable and automated
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.
Build the network foundation for distributed inference
Combine IP, optical, security and automation capabilities to connect distributed inference resources across data centers, clouds and provider domains.
High-performance IP routing
Scale connectivity between inference locations while supporting traffic engineering, service differentiation, telemetry and resilient operations.
Scalable, intelligent optical networking
Move high-volume AI traffic efficiently between sites with high-capacity transport and operational simplicity.
Multilayered network security
Protect inference connectivity with segmentation, encryption, DDoS protection and policy-based controls.
Programmable network automation
Use open APIs, model-driven operations and assurance to provision, observe and adapt connectivity as inference demand and workload placement change.
Learn more
Why Nokia?
Nokia brings together expertise in IP routing, optical networking, network security, automation, mobile and fixed networks and open network architectures. We work with customers, partners and the broader ecosystem to design and build trusted connectivity for distributed AI infrastructure.
For inference connectivity, this means connecting AI resources across sites, clouds and provider domains securely, predictably and at scale, while creating a foundation for automation and cross-domain service delivery.
Frequently asked questions
Distributed inference places AI inference resources across multiple locations and directs workloads to the environment that best meets application, performance, capacity, cost, operational or sovereignty requirements. It complements centralized inference in cloud data centers by adding deployment choice closer to users, enterprises, devices and data sources.
Inference connectivity is the secure, scalable and programmable network layer that connects distributed inference resources across sites, clouds and provider domains. It helps maintain predictable performance, availability, isolation and operational visibility as traffic patterns and workload placement change.
Ordinary connectivity provides reachability and transport. Inference connectivity adds the service characteristics needed for distributed AI, including traffic engineering, measurable service levels, resilient paths, tenant isolation, real-time telemetry, automation and coordination across infrastructure domains.
Distributed inference does not replace centralized inference. Instead, distributed and centralized inference are complementary. Workloads can run in centralized clouds, regional facilities, provider infrastructure or enterprise locations according to application and policy requirements. Inference connectivity provides the connectivity foundation for using these tiers as part of a coordinated service.
Inference connectivity can use private connectivity, segmentation, encryption and policy-based paths to help control where data travels and which infrastructure domains it crosses. These network controls support sovereignty strategies, but compliance also depends on the compute platform, data architecture, operating model and applicable regulation.
Telecommunication providers can combine wide-area connectivity, distributed sites, local market presence and service assurance to connect inference resources closer to users, enterprises and data sources. They can also enable services that span multiple compute and provider environments.
AI and cloud providers can use inference connectivity to reach distributed capacity and customer locations while maintaining private connectivity, predictable service levels and operational visibility. This can support deployment across locations selected for performance, capacity, economics, resilience or sovereignty.
Related topics
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