Networking for AI workloads

The Nokia Data Center Fabric solution provides the reliability, simplicity and flexibility you need to build and deploy network infrastructures that can meet the requirements of current and future AI workloads.
 

Modular and fixed-configuration platforms

Our solution includes a comprehensive portfolio of modular and fixed-configuration hardware platforms for implementing high-performance leaf–spine designs. You can use our platforms to build high-capacity, low-latency and lossless back-end networks that can efficiently handle demanding AI training workflows. We also offer platforms in many different form factors to support front-end network designs that will interconnect your AI inference compute, non-AI compute and shared storage resources.
 

A powerful and proven network operating system

Our data center hardware platforms run on the Nokia SR Linux network operating system (NOS). SR Linux opens the NOS infrastructure with a unique architecture built around model-driven management and modern interfaces. It is designed for reliability and quality, ready for automation at scale, and easy to customize and extend. 

SR Linux provides congestion management and traffic prioritization capabilities that let you deliver lossless Ethernet networking. It also supports high-performance AI infrastructures with superior telemetry, manageability, ease of automation and resiliency features.
 

Fabric management and automation toolkit

Nokia Event-Driven Automation (EDA) is a modern data center network automation platform that combines speed, reliability and simplicity. It makes network automation more trustable and easier to use wherever you need it, from small edge clouds to the largest data centers.

With EDA, you can automate the entire data center network lifecycle from initial design to deployment to daily operations. This allows you to ensure reliable network operations, simplify network management and adapt to evolving demands.
 

Data center interconnectivity

Some companies may implement their own training infrastructures. Others may prefer to use AI platforms and associated services from large cloud providers, an approach known as GPU as a service (GPUaaS) or public AI.

Inferencing models that need low-latency end-user access are typically run in enterprise edge locations in private AI infrastructures. Batch-mode and global-scale inferencing models are better suited for implementation in public AI frameworks.

We offer a comprehensive solution that makes it easy to interconnect AI infrastructures between data centers and across the WAN. The Nokia Optical Data Center Interconnect (DCI) and Nokia Data Center Gateway (DCGW) solutions let you provide reliable, high-performance interconnection across AI infrastructure domains. They can help you meet evolving distributed AI connectivity requirements so you can serve AI models at the edge of the network, closer to end users.