Eliminate uncertainty, complexity and risk with Nokia Validated Designs
Validated designs have been around for a long time. In many organizations, however, they often amount to little more than a documentation exercise—static diagrams and configuration guides that look good on paper but rarely translate cleanly into real-world deployments.
The true value of a validated design emerges only when it supports a realistic, simple and repeatable process. This is the guiding principle for Nokia Validated Designs (NVDs).
Years ago, Mike Bushong, our VP of Data Center at Nokia, in public commentary on network operations, said that that if we built airplanes the way we build networks, we’d expect them to fall from the sky (paraphrased). What he meant was that networks should be engineered and tested with such rigor that an outage would become as rare and unacceptable as a plane crash. We take this philosophy seriously.
Our goal with NVDs is to apply aerospace-level scrutiny to data center networking so our customers can deploy with confidence rather than hope.
A rigorous validation approach
Our validation approach centers on Nokia SR Linux, a highly stable, modern network operating system (NOS). With SR Linux, the likelihood of encountering critical defects is extremely low. Still, we don’t rely on probabilities or assumptions.
We test every NVD from end to end. At the control plane level, we use our digital twin to model behavior and failure scenarios. At the data plane level, we use a full physical fabric to validate real traffic and performance characteristics.
Individual features are tested in isolation, and then again as part of the complete solution. We don’t automate until the full design has passed validation. This ensures that customers can deploy these architectures with minimal effort and without guesswork.
With this combination of deep validation and automation, we turn a design from a document into a deployable system.
AI NVDs: Going beyond the fabric
For AI-focused NVDs, we deliberately went a step further.
AI data centers are fundamentally different from traditional enterprise or cloud data centers. In a conventional data center, networks are typically contiguous and fully interconnected. In an AI data center, this is often not the case.
AI environments frequently consist of separate front-end and back-end networks that are not directly connected to each other. The only devices that participate in both networks are the graphics processing unit (GPU) servers. As a result, GPU servers become an integral part of the system under test, not just endpoints hanging off the fabric. Because of this, our AI NVD validation process explicitly includes the GPU layer.
Incremental, full-stack validation
The AI NVD test methodology is intentionally incremental:
- Hardware compatibility validation: We begin by validating server, network interface card (NIC), GPU and fabric interoperability.
- Network layer connectivity testing: We test connectivity to ensure correct IP fabric behavior, routing, loss characteristics and scale.
- Collective communications validation: We then validate the software layers that make large-scale distributed AI training possible: NVIDIA Collective Communications Library (NCCL) and Radeon Collective Communications Library (RCCL). These collectives determine how workloads are distributed, how GPU ranks are assigned and how efficiently GPUs communicate during training and inference.
- Workload benchmarking: Once the fabric and servers are optimized for collective performance, we benchmark real training and inference jobs using industry-standard tests defined by MLCommons.
By validating the entire stack from network hardware through distributed AI workloads, we provide customers with actionable, reproducible results rather than abstract performance claims.
Readily downloadable, repeatable documentation
The documentation that accompanies NVDs follows the same philosophy as the validation.
We start by explaining the core constructs and technologies involved in AI data centers. From there, we:
- Show how to orchestrate each segment of the architecture.
- Document the validation tests we executed.
- Enable customers to repeat those validations to verify their own deployments.
- Provide automation frameworks to deploy the full cluster consistently.
The result is both a reference guide and a practical blueprint for deployment and optimization.
Recent examples of AI NVDs include:
- A validated design developed with Lenovo, focusing on sovereign AI cloud architectures.
- A validated design based on AMD GPUs, later extended to include AMD Pensando Pollara NICs.
- A validated design based on NVIDIA GPUs, optimized for large-scale distributed training.
Across these efforts, the objective remains the same: If customers choose Nokia for their data center fabric, we should equip them with complete, end-to-end knowledge of how to deploy, validate and optimize their AI clusters.
The path to human error zero
In a world where data is everything, organizations are increasingly constrained by complex multivendor integrations, long deployment cycles and the ever-present risk of human error.
NVDs address these challenges head-on by removing uncertainty from infrastructure deployment. Through the NVD program, we help customers:
- Avoid incompatible component combinations.
- Eliminate costly trial-and-error testing.
- Bridge operational skills gaps.
- Meet aggressive deployment timelines with confidence.
With rigorously validated, automation-ready architectures, human error zero becomes achievable.
Evolving for current and future demands
We are not standing still. All released NVDs undergo continuous regression testing, and we are actively expanding validation to cover emerging AI and data center technologies. Areas currently under investigation include:
- Large-scale multi-plane AI architectures.
- AI-optimized scale-across networking.
- Data center interconnect (DCI) for AI workloads.
- Next-generation GPU and accelerator platforms.
As AI infrastructure continues to evolve, Nokia Validated Designs will evolve with it.
If you’d like to continue the conversation or explore a specific NVD in more detail, feel free to contact me through my LinkedIn account.
