Skild says Steel hit 3.90 million environment steps per second on 20 mixed Isaac Lab tasks at 245,760 environments. At a matched batch size of 61,440, Steel reached 3.61 million steps per second and used about 11 GiB of GPU memory to PhysX's 29 GiB.
By [Ryan Merket](https://runtimewire.com/author/ryan-merket)
· Published
Primary source: [x.com](https://x.com/SkildAI/status/2119844954893946999)
Why it matters #
Steel addresses the cost of simulating many different manipulation tasks at once, a bottleneck for training general-purpose robot policies. Skild has published the code, the solver design, and benchmark conditions, so outside teams can test the peak throughput figure and memory footprint.
Skild AI open-sourced Steel on November 9th, a GPU-accelerated rigid-body physics solver built with NVIDIA for training robots on diverse manipulation tasks. Skild AI (@SkildAI) announced the release in a thread on X, saying Steel runs up to 13x faster than PhysX while using roughly 3x less GPU memory in its heterogeneous test.
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