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arXiv · 2611.05104

Workload-Static Optical Circuit Switching For Scalable LLM Inference Decode

Maximilian Thürmer·Nicolas Scotto di Perto·Eloise Stein·Natanijel Vasic·Mikaeil Bagherli·Zhenguo Wu·Benjamin Klenk·Holger Fröning·Johannes Feldmann
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Abstract

LLM inference decode executes fine-grained collectives whose latency directly limits per-user interactivity, yet scale-up fabrics are still built from electronic packet switches designed for training. We observe that, for a given model deployment and parallelization, the communication structure of decode is fixed, and propose workload-static optical circuit switching (OCS): circuits are configured once per deployment, co-designed with model parallelism and with topology-aware collective schedules that turn spare accelerator ports into fewer communication phases or higher bandwidth. We evaluate this design with GXNET, a graph-based workload emulator coupled to analytical and flit-level network models grounded in hardware measurements. In a 576-accelerator scale-up domain serving 5T-parameter MoE models with up to 5M tokens of context, all-OCS fabrics improve interactivity by up to 65% and energy efficiency by up to 119% over 4:1 oversubscribed packet-switched fabrics.

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Maximilian Thürmer, Nicolas Scotto di Perto, Eloise Stein, Natanijel Vasic, Mikaeil Bagherli, Zhenguo Wu, Benjamin Klenk, Holger Fröning, Johannes Feldmann. 2026-11-03. Workload-Static Optical Circuit Switching For Scalable LLM Inference Decode. https://arxiv.org/abs/2611.05104

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