Technical detail · Bandwidth limits and compartmentalization

Pod and cap. Lucid Computing's Traffic Shaping design models a pod as one NVIDIA GB200 NVL72 rack (72 GPUs). A shaper at each pod's north-south uplink enforces a recommended cap of β = 1 Gbps (125 MB/s) per direction per pod, averaged over 60 seconds, about 14 Mbps per GPU ².
Adversary. The reference served model is a "Fable-sized model (estimated 6T parameter MoE)"; the adversary's target is about 10x larger (60T parameters). Strategies analysed: centralized training adapted to the cap, DiLoCo-family low-communication data parallelism (including streaming and quantized variants), and SWARM-family decentralized pipeline parallelism. The analysis grants the adversary perfect overlap of communication and computation, zero pipeline bubbles, zero straggler and fault overhead, free coordination compute outside the pods, aggressive low-precision recipes and the best published communication-reduction techniques ².
Results. The headline is a lower bound on the inefficiency multiplier of about 350x, with central estimates of 1,000x–10,000x depending on training architecture. For SWARM-style pipelines the floor falls to about 140x only if two further unproven techniques both succeed (8x lossless activation compression at frontier scale and an unusually narrow 60T-parameter architecture). The appendix summary claims at least 100x, with roughly 1.4–3.5x of margin, after stacking every charitable and speculative assumption ².
Conditions. The auditor randomizes the assignment of pods to routers and re-randomizes it periodically, and per-pod local storage is declared, capped and inspected. If the operator could instead assign whole cells of 100 or more pods to one pipeline stage, the bound would collapse to about 90–220x uncompressed and as low as about 25x with compression ².
Amodo's node-level limiter. NVIDIA BlueField-3 DPUs replace the NICs on a 400G Ethernet link, reaching 390 Gbps unencrypted and 193 Gbps encrypted between two DPUs ⁷.