Building Modern & Scalable DC Fabrics#

Speakers: Alperen Akpinar (Nokia)

• Why: A useful one-slide-deep map of how AI workloads are rewriting DC fabric requirements — worth the PM's time for the framing and the terminology even though it is a Nokia sales-informed deck. It lays out the scale-up / scale-out / scale-across taxonomy and the specific feature checklist an "AI-ready" Ethernet fabric now needs (DLB, RDMA-aware hashing, DCQCN, multi-plane topologies), which is exactly the vocabulary for evaluating any vendor's AI-networking claims — including Nokia's competitors'.
• What: A technology-evolution and requirements talk: speed progression (10/100M → 800G+/1.6T) alongside a protocol progression (STP → BGP/EVPN-VXLAN → UET/uSIDs/DCQCN); AI resetting DC scale (Meta Luleå 30 MW in 2013 → Altoona 500 MW in 2025 → gigawatt-class in 2026); and the three-axis scaling model — scale-up (Nvidia NVLink/NVSwitch, AMD UALink, Broadcom SUE, OCP ESUN), scale-out (InfiniBand, RoCEv2, UEC), scale-across (DCI via 800G ZR/ZR+ coherent optics in C+L band over ~1000 km, 4x400G FlexE bundles, RDMA over WAN, encryption). Concrete detail on GB200 NVL72 intra-rack copper fabric (9 NVLink switches, 18 chips, 144 ports at 100GB/s, 1.8TB/s per GPU) and on why AI traffic differs from traditional apps (imbalanced flows → tail latency → job completion time dominated by slowest GPU). The Nokia product content maps requirements to platforms: 800G/1.6T backend fabrics on 7220 IXR-H5/H6 and 7250 IXR Gen3+, DLB and RDMA-aware (Q-pair) hashing, DCQCN (PFC/ECN) plus MRC, congestion-indicator telemetry, backend multi-tenancy, EDA automation, and deployment archetypes from inference edge clusters through 1K–16K-GPU two-tier clusters to 100K–1M-GPU multi-plane "AI factories."
• How: Conceptual/architectural rather than a mechanism — the value is the requirements taxonomy (scalable, lossless, automated for in-DC; maximum capacity and high-frequency congestion-aware steering for DC-to-DC), not any single technique. No empirical evaluation is presented.
• Where applicable: The landscape framing is generally applicable to anyone designing or buying AI-era fabrics; the product mapping, feature claims (e.g., DLB/Q-pair behavior, telemetry capabilities), and deployment examples are Nokia-specific and unvalidated — treat them as a vendor's positioning to be checked against your own bake-offs and silicon. Based on the speaker's slides.