Ilker ManapandClaude Opus 5.5
f2c6f12cfb
CI / test (push) Successful in 9m1s
For each layout modelplan.Options offers here — one card, tensor parallel across a node's cards, llama.cpp's layer split, a pipeline across nodes on a fabric — modelplan.GPUTokensPerSecond gives one reply's speed: the bytes a token reads over each card's bandwidth, by its model name (a table, SXM and PCIe apart), plus two all-reduces a layer for tensor parallel (8 us on NVLink, 25 us and up on PCIe), turns between cards for a layer split, and a hop per node over InfiniBand or RoCE for a pipeline. The explorer and a repository's files show the fastest, with the layout; mixed classes are judged class by class, FP8 on 8.9 cards only. Calibrated on cuda01's Quadro RTX 6000 (672 GB/s): qwen3-vl-8b Q8, 8.7 GB, 62.1 tok/s measured, 59 estimated; Qwen3-Coder-30B-A3B Q4_K_M, 18.6 GB, 144 measured, which says a model of experts reads 1.6 times its active share (ActiveShare now does, on the CPU too). Tensor parallel and fabrics are not measured here. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>