Many physical AI startups hit a wall once their product ships: data streams in faster than teams can label, every model update must be validated before it reaches hardware, and the simulation‑to‑real loop shifts from experiment to a required release step. The 2026 Nebius Physical AI Awards are built for this exact stage. The program invites startups that already have a demonstrable product, real traction, and a clear scaling plan to compete for five category prizes, each worth $150 000 in Nebius compute credits. Applications are open now and close on October 25 2026; finalists and winners will be announced in mid‑November 2026. There is no entry fee and no sponsored tracks.
Winners receive the compute credit which, at Nebius’ published on‑demand rates, translates to roughly 38 960 GPU‑hours on an H100 node, 33 330 GPU‑hours on an H200 node, 20 980 GPU‑hours on a B200 node or 19 100 GPU‑hours on a B300 node. For a team already running multi‑node training, $150 K on H200s provides about five hundred twenty‑one hours on a 64‑GPU cluster – roughly three weeks of continuous work – and at preemptible rates it stretches to over sixty thousand GPU‑hours, ideal for checkpointable reinforcement learning or data‑generation jobs. Alongside the credits winners get joint promotion from Nebius and NVIDIA, an executive mentorship session with a Nebius C‑level leader, a Winners Circle feature article, and two seats at an exclusive executive dinner next year with leaders from the physical AI ecosystem.
Applicants choose the single category that best matches their strongest product: Physical AI Models, Perception & Spatial Intelligence, Simulation & Synthetic Data, Systems & Deployment, or Software Tooling & Orchestration. Eligibility requires a clear physical AI use case, an MVP in active use or testing, a registered legal entity, and a live public website. Each submission covers one product. The judging panel, selected by Nebius and NVIDIA, scores on depth of innovation, breadth of implementation, market potential, and real‑world impact, with finalists decided by a jury vote.
If your team is ready to turn field data into faster, safer model updates, now is the time to apply and secure the compute needed to scale.
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