published scale-out oversubscription examples include 1:1, 2:1–3:1 and a reported 7:1 deployment; they are workload-specific observations, not training/inference topology defaults
1:1 vs 2:1–3:1observedcontested
| Value kind | observed — Reported measurements, counts, and specifications keep the precision and scope stated by their source; an exact specification is not treated as a range. |
|---|---|
| Scope | named reference designs and deployments with different traffic, topology, placement and service objectives |
| Caveat | Derive oversubscription from the measured traffic matrix, collective/request mix, topology, failure headroom and SLO; validate it on the target fabric. |
| As of | 2025 |
| Source | SemiAnalysis Neocloud Playbook; Juniper AI-cluster design; Meta |
| Review | checking…review by 2026-08-25 · fast cadence |
| Recorded changes | last 2026-06-30 · 2 revisions tracked |
| Claim id | training-non-blocking-vs-inference-2 |
Where the guide uses it
- 1.7 The Requirements-and-Consequences Matrix
- 8.1 Network Fundamentals & AI Traffic Characterization
- 8.4 Scale-Out Fabric: Protocols, Standards & Transport
- 8.5 Scale-Out Topology, Sizing & Oversubscription
← Full numbers register — every date-stamped figure in the guide, with revision history.