Dreamscale Labs (YC F26)

I just recreated @GeneralistAI’s egg demo, end to end on Dreamscale 1/5

What does it take to run π0.5 in the cloud with zero stalls and imperceptible latency? For us: • Custom kernels • Custom network transport • Dedicated post-training to account for latency For you: • 1-line CLI command • Serverless compute that spins up in 2 seconds • Don't pay for idle usage See for yourself: (1x video in thread)

Physical AI gets so complicated at times ... We just launched the easiest way to run frontier physical AI models on your robots - the UI can't get any simpler: I want to run a <model> on a <robot> to <do something> No GPU, pure real-time inference over the cloud. Live now at: dreamscalelabs.com

We just ran one of the largest robot models that exists today, all without a local GPU. And it’s running real-time at <50 ms overhead. Scaling laws are becoming real for physical AI. Perceptron’s Isaac 0.5 is one of the largest open-source models, sitting at 36B total parameters. @perceptroninc joins other frontier robotics labs like @DynaRobotics and @GeneralistAI in showing the need to massively scale pre-training data, from a 1000s to millions of hours of video. Specifically, they show that the more egocentric video the model is trained on, the less teleop data that it needs to achieve the same loss. At @DreamscaleLabs (YC F26), we’ve noticed an important truth here: data and training compute do not just scale in isolation. The model sizes must increase too, and GPT-6 Astra has given a glimpse into what a trillion-parameter robotics model could do. We’re building to serve that future - one that is not constrained by the bottlenecks of on-board compute, but rather, allowing large models to run fast over the cloud. Reach out if you’re interested in our work.

@DreamscaleLabs (YC F26) is the first inference provider to serve NVIDIA’s DreamZero robot model over the cloud! And we beat @nvidia's own reported inference speed by 8%, with just half the silicon. DreamZero is a model that pioneered the new wave of “World Action Models” that has taken over generalist robotics today. It promises to generalize better to new tasks, learn physics from human video, and transfer well to new embodiments. Normally, DreamZero requires 2x GB200 superchips (each costing $60K) or is painfully slow (waits 5.7s before moving 1.6s), which made it infeasible to deploy to robots in practice. Now, with Dreamscale’s real-time cloud inference infrastructure, we’ve demonstrated fast, smooth motion on actual robots with sub-second latency. As scaling laws become more relevant in robotics, frontier physical AI models are growing more computationally expensive than ever. With Dreamscale, we are finally making World Action Models (WAMs) accessible to startups and robot developers across the world. Reach out if you are interested in cloud robot inference.

Today we're incredibly excited to announce @DreamscaleLabs (YC F26)! We're an applied research company putting robot brains in the cloud. Robot AI models are growing larger and on-board compute is getting increasingly inaccessible due to cost. We're taking a contrarian bet that the future is moving inference off the robot and into the cloud, while still meeting real-time deadlines required by robot motion. We're building the real-time inference infrastructure that enables frontier physical AI models to run smoothly on your robot. We optimize for low latency across the full stack: from the model, down to the GPU kernels, and over the network. Our mission is to unlock a future of safe and intelligent robots doing meaningful work, and we think this is only possible by letting you deploy and run robot models in the cloud. Reach out to us at [email protected] or visit our website at dreamscalelabs.com to learn more. @KDog112358 @AntoineNeedGPUs @eternaI_entropy