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Get an inside look at how Nokia Validated Designs (NVDs) streamline AI-ready data center and networking deployments through proven architectures, rigorous validation, and real-world performance insights. We’ll highlight several of our latest AI-focused NVDs, show how partners are extending them, and preview what’s coming next as we evolve the portfolio to meet the demands of modern, high-performance networks. Vivek Venugopal explains that Nokia treats network construction with the same intolerance for failure as aeronautical engineering, ensuring that every NVD is pre-tested on physical hardware to guarantee reliability. These designs are developed through an iterative workflow that begins with industry ideation and digital twin modeling in container labs, followed by extensive hardware validation of optics, cables, and protocols. Unlike rigid templates, NVDs serve as documented tech stacks that customers can customize, backed by a four-year support lifecycle that treats the design itself as a managed product.
The presentation highlights several AI-specific architectures, including a rail-only design developed with Lenovo and AMD for small-to-medium clusters and a more complex two-stripe pod design for larger environments using NVIDIA H200 or AMD GPUs. A key innovation discussed is the use of VRFs to emulate multiple leaf switches, allowing customers to scale their GPU clusters accurately without over-provisioning hardware. To ensure these networks are truly lossless, Nokia rigorously validates the interaction between Explicit Congestion Notification (ECN) and Priority Flow Control (PFC). The goal is to ensure ECN triggers first to slow down traffic before PFC pauses frames, preventing the catastrophic tail drops that would force an AI training model to restart from a previous checkpoint.
To prove the real-world efficacy of these designs, Nokia goes beyond simple network specifications to perform application-level benchmarking using open-source tools like Llama 2 and BERT. By measuring job completion times and tokens per second against MLCommons standards, they provide a full-stack validation that includes the GPU servers, storage fabrics (using partners like VAST Data or DDN), and the backend network fabric. The NVD roadmap continues to expand with upcoming designs for scale-across architectures, multi-plane fabrics, and storage-focused deployments. All automation playbooks, telemetry stacks, and digital twin models are made available on GitHub, allowing engineers to try before they buy and ensuring the designs remain accessible and open for integration with common frameworks like Ansible and Netbox.
Personnel: Vivek Venugopal
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