Multi-GPU collaborators wanted

The manuscript deliberately stops at a small CPU-friendly feasibility check. This repository turns the next scientific question into a reproducible scaling benchmark.

We are looking for contributors with 2, 4, 8, 16+ GPUs and multi-node systems, especially H100/H200, B100/B200, GB200/GB300, MI300X-class, or comparable accelerators. Useful contributions include:

strong- and weak-scaling runs at 128/256/512 resolution;
larger low-rank transition ranks (32/64/128+);
long-horizon rollout extensions and stability measurements;
Moving MNIST, DMControl pixels, Habitat, Atari-like, or physical-video adapters;
matched-compute Dreamer/PlaNet, Transformer, Koopman, and neural-operator baselines;
profiling of throughput, peak memory, communication overhead, and operator kernels.

Minimal contribution

Fork/clone the repository and install requirements.txt.
Run ./scripts/smoke_test.sh.
Run, for example, GPUS=8 RES=256 BATCH=16 ./scripts/run_scaling_sweep.sh.
Add the generated JSON files and results/leaderboard.csv under a hardware-specific folder.
Open a PR with GPU model/count, CUDA/ROCm, PyTorch version, node topology, command, and any code changes.

Please do not report only the best run. Repeated seeds and failed/OOM configurations are scientifically useful.