PhyAI: Real-Time Physical AI at the Edge, Scalable Rollouts in the Cloud
Authors:
Chenghua Wang,
Daliang Xu,
Dongqi Cai,
Duojin Sun,
Hao Zhang,
Haoze Qian,
Huaiyuan Zhang,
Jinshuo Cui,
Junbo Cui,
Kezhao Zhao,
Longxi Gao,
Mengwei Xu,
Rongjie Yi,
Ruixin Liu,
Shangguang Wang,
Tam Sikyuen,
Tianyue Zhang,
Weikai Xie,
Xuanzhe Liu,
Yingying Qin,
Yiwen Lu,
Yuan Yao,
Yuezhi Zu,
Yunhan Guo,
Yuxin Zheng
, et al. (1 additional authors not shown)
Abstract:
Physical AI policies require inference throughout their lifecycle, including model evaluation, cloud reinforcement learning rollout, edge GPU serving, and onboard deployment. Although these settings share the same checkpoint and action semantics, they often rely on separate inference programs. To unify them, we build PhyAI, a Physical AI inference engine with a single runtime that keeps architectu…
▽ More
Physical AI policies require inference throughout their lifecycle, including model evaluation, cloud reinforcement learning rollout, edge GPU serving, and onboard deployment. Although these settings share the same checkpoint and action semantics, they often rely on separate inference programs. To unify them, we build PhyAI, a Physical AI inference engine with a single runtime that keeps architecture-specific conditioning, solver, cache, and output logic in model adapters while sharing graph execution, kernels, memory management, and parallel services. The same codebase runs vision-language-action (VLA) models and world-action models (WAMs) on single or multiple GPUs across onboard, edge, and cloud deployments. We used the adapter interface to add MiniCPM-Robot on the day of its release. PhyAI achieves 1.40x-4.65x speedups over the official implementations of pi0, pi0.5, GR00T N1.7, and MiniCPM-Robot. On Cosmos3-Nano-Policy-DROID it reduces latency from 2.46 to 1.18 s on eight H20 GPUs (CFG=2, TP=4), a 2.08x speedup. Specialized runtimes remain faster in several configurations, so our goal is one runtime with competitive latency rather than the fastest result in every case. Detailed profiles reveal why different models need different execution policies: on a Hopper-series GPU at batch size one, the pi0.5 action expert accounts for 8.8% of FLOPs but 57.2% of latency; at batch size 32 its share drops to 13.5% and throughput reaches about 100 samples/s. Cosmos3 remains generation-dominated and gains only 14.3% throughput as batch size increases from 1 to 16. We further introduce the control-time Roofline, which distinguishes inference-bound from environment-bound control; the measured pi0.5 points on four LIBERO suites are environment-bound while Cosmos3 stays inference-bound. Code and benchmarks: https://github.com/mingti-org/phyai.
△ Less
Submitted 14 August, 2026; v1 submitted 4 August, 2026;
originally announced August 2026.