rolv.ai publishing reproducible benchmarks showing that ROLV outperforms vendor dense kernels and vendor sparse libraries across GPUs, CPUs, and TPUs while prod

rolv.ai publishing reproducible benchmarks showing that ROLV outperforms vendor dense kernels and vendor sparse libraries across GPUs, CPUs, and TPUs while producing the same hash/artifact on every platform. Highlights: commodity Intel Xeon Kimi K2.5 expert FFN slice runs 228.38 ms → 6.36 ms per iteration (35.9×), token throughput 2,240 → 80,500 t/s, and kernel energy drops 97.8%. Nsight‑validated traces and full methodology are in the PDF below; results cover LLM FFNs, recommendation GEMMs, FEM, GNNs, mobile and EV proxies, and production subsamples. We welcome technical critique on methodology, requests for additional baselines, and calls for the exact scripts to reproduce any specific run.