When Does Distributed AI Inference Need More Wide-Area Bandwidth? A Co-Design Evaluation of Optical, Packet, and Software Levers

[2608.14967] When Does Distributed AI Inference Need More Wide-Area Bandwidth? A Co-Design Evaluation of Optical, Packet, and Software Levers Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2608.14967 (cs) [Submitted on 15 Aug 2026] Title: When Does Distributed AI Inference Need More Wide-Area Bandwidth? A Co-Design Evaluation of Optical, Packet, and Software Levers Authors: Prasanna C View a PDF of the paper titled When Does Distributed AI Inference Need More Wide-Area Bandwidth? A Co-Design Evaluation of Optical, Packet, and Software Levers, by Prasanna C View PDF HTML (experimental) Abstract: Wide-area bandwidth per unit of GPU compute falls every hardware generation: in compute-intensity-ratio terms (CIR, bytes per FLOP), the gap between on-package memory and the conventional WAN is four to five orders of magnitude, widening at roughly 12-19% per year. Position papers - including our own - argued this makes elastic optical wide-area capacity necessary for cross-site AI inference. Reviewers correctly objected that such arguments show more bandwidth helps, not that it beats the alternatives: KV recomputation, cache compression, locality-aware routing, scheduling, or an overprovisioned packet backbone. This paper does the comparison. We derive a workload model predicting when moving inference state across sites beats recomputing it - a context-independent crossover at 74-111 Gbps per stream for a 70B multi-head-attention model, falling to 9-14 Gbps under grouped-query attention at 1/8 KV heads - and quantify five sensitivity axes: context length, attention architecture, queueing, agentic compounding, and loss/jitter-induced bandwidth collapse. On economics: at list GPU prices recomputation is cheaper; transfer wins when GPU scarcity and KV reuse multiply effective GPU cost by roughly 5-20x, and modern attention moves the breakeven an order of magnitude in transfer's favour. We position the network levers correctly rather than adversarially: packet networks allocate lit capacity at millisecond timescales; optical fungibility changes how much capacity is lit, at minute timescales, substituting for overprovisioning economically rather than functionally. Finally we specify a ten-metric measurement plan on a three-site production-fibre testbed, framed as an open ecosystem exercise: no single company can - or should - assemble this evidence alone. Every claim is bounded by the regime in which it holds; several findings weaken the naive version of our own thesis, and we state them. Comments: 10 pages, 4 figures, 3 tables Subjects: Distributed, Parallel, and Cluster Computing (cs.DC) ; Networking and Internet Architecture (cs.NI) ACM classes: C.2.1; C.2.5; C.4 Cite as: arXiv:2608.14967 [cs.DC] (or arXiv:2608.14967v1 [cs.DC] for this version) https://doi.org/10.48550/arXiv.2608.14967 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Prasanna Caliaperoumal [ view email ] [v1] Sat, 15 Aug 2026 01:39:05 UTC (386 KB) Full-text links: Access Paper: View a PDF of the paper titled When Does Distributed AI Inference Need More Wide-Area Bandwidth? A Co-Design Evaluation of Optical, Packet, and Software Levers, by Prasanna C View PDF HTML (experimental) TeX Source view license Current browse context: cs.DC < prev | next > new | recent | 2026-08 Change to browse by: cs cs.NI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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