Infinigence AI, in collaboration with teams from Tsinghua University and Shanghai Jiao Tong University, has released APXInf to the public. This engine is designed for edge inference and is built for embodied models that must close the perception-decision-action loop directly on the robot, rather than in the cloud.
The public package is available on GitHub at RLinf/APXinf-robo and includes a Rust runtime with Python bindings, optimized for Jetson class devices and desktop GPUs. The company positions this release as the 'last mile' between capable vision-language-action models and stable control directly on the robot.
According to Infinigence data and coverage by QbitAI, using the FP8 format with PI 0.5 on the NVIDIA Jetson Thor platform reduces end-to-end inference time from approximately 278 ms to less than 26 ms. This achieves a frequency of about 38.46 Hz, which is sufficient for real-time multi-scenario control. This improvement represents an approximate tenfold reduction in latency compared to the unoptimized baseline they cite.
This breakthrough was made possible through work on the pipeline, graph, kernel, and quantization, as well as the use of Agent4Kernel, an agent-assisted CUDA kernel fusion.
Initial model support includes PI 0.5 and WALL-OSS. Target hardware platforms include Jetson Orin, Jetson Thor, and GeForce RTX 4090. Architectural decisions emphasize long-term stability alongside peak speed: a minimal Rust runtime is used for memory safety, complemented by Python ergonomics for developers. Furthermore, an agent workflow has been implemented to adapt new VLA/WAM checkpoints without requiring manual writing of every kernel path.
This engine integrates with Infinigence's earlier RLinf reinforcement learning training stack, extending the same open ecosystem from post-training to deployment on the robot with OpenPI-compatible servicing capabilities.
Future development plans include ports for a larger number of VLA/VLM and world models (work with Qwen and GR00T classes is already planned), optimization of nvfp4, development of backends for AMD, and support for domestic chips for inference and operating systems for domestic robots. Teams are encouraged to independently reproduce the latency metrics for Thor/Orin on their own checkpoints and within specified power consumption parameters. The stated FP8 value below 26 ms sets a standard for embodied system inference on the edge but is not a guarantee for every model or package form.