Without getting all the way down to performance counters, GPU power from nvidia-smi is a better indicator of true utilization than job scheduling or “gpu busy”. I would love to see animated “heat maps” of the big data centers, with each pixel being an individual GPU’s power draw. I am confident that inference and frontier training at the big labs is highly efficient, but I wonder how many GPUs would be dark due to scheduling and inefficient research code. With a little calibration for base load and peak, just the power bill for the datacenter would be a pretty good first order indicator of utilization.

Paper review: LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels arxiv.org/pdf/2603.19312 Nice clean github: github.com/lucas-maes/le-wm This is the application of the LeJEPA results to world models, trained offline on experience from three different robotics style tests with one to two million steps in each dataset. Re-states the benefits of the SigReg loss relative to prior world model approaches. Uses ImageNet standard 224x224 RGB pixel input images with an unmodified ViT-Tiny vision transformer from HuggingFace to generate latents. One extra post-projection step is needed to give SigReg the necessary freedom to perturb the latents into independent gaussians, since ViT ends with a layernorm’d layer. Also tested with ResNet-18, which still performed well, but slightly worse. Uses a 192 dimensional latent. Performance slightly dropped when doubling the latent size to 384; it would be nice to know if it was stable there, or if it continued worsening with excessive latents. There is a relationship between batch size and SIGReg, the larger latent may have improved performance if the batch size was increased. The predictor is implemented as a ViT-S backbone – Why a vision transformer when the latent is flat? Uses a history of 3 sets of latents for two of the benchmarks and 1 for the other. Performance was markedly better with the “small” ViT model than the “tiny”, but the larger “base” model degraded notably, which is interesting. Dropout of 0.1 on the predictor significantly improved performance. 0.2 was still better than 0.0, but 0.5 was worse. Trained with a batch of 128 x 4 trajectories. I wish their training loss graphs were more zoomed in with grid lines. Performs planning at test time instead of building a policy by training in imagination like Dreamer / Diamond. Rolls out 300 initially random sets of actions up to a planning horizon H of 5 (at frame-skip 5). Iterates up to 30 times using the Cross Entropy Method (CEM). The main paper body mentions using Model Predictive Control (MPC) strategy, where only the first K planned actions are executed before replanning, but appendix D says they execute all 5 planned actions. After training, they probe the latent space to demonstrate that it does capture and represent physically meaningful quantities. They also implement a decoder from the latent space back to pixels – not used by the algorithms, but helpful to see what things the latent space is actually representing. They tested incorporating the reconstruction loss into training, but it hurt performance somewhat. They wound up with a 0.1 lambda for SigReg, as opposed to 0.05 in the LeJEPA paper. 1024 sigreg projections, but observe the number has negligible impact I like the JEPA framework, but so far my attempts to use it on Atari games with value functions have not matched my other efforts.