无问芯穹联合清华、上交正式开源具身端侧推理引擎APXInf，Pi 0.5性能SOTA
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Merged summary
TL;DR - Infinigence AI, Tsinghua University, and Shanghai Jiao Tong University open-sourced APXInf, an edge inference engine for deploying embodied AI models on robots. It reportedly cuts π0.5 FP8 end-to-end latency on Jetson Thor from 278 ms to under 26 ms, enabling 38.46 Hz real-time inference.
- APXInf targets low-batch, low-latency, resource-constrained robotics workloads through pipeline, graph, kernel, and quantization optimizations.
- The engine uses a minimal Rust runtime for memory safety and predictable operation, with Python interfaces for easier model integration.
- Its initial release supports π0.5 and WALL-OSS on Jetson Orin, Jetson Thor, and RTX 4090 hardware.
- The roadmap includes VLA, VLM, and world models; Qwen and GR00T support; NVFP4 optimization; and additional domestic and AMD hardware backends.
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无问芯穹联合清华、上交正式开源具身端侧推理引擎APXInf，Pi 0.5性能SOTA
Public signals
N/A
TL;DR - Infinigence AI, Tsinghua University, and Shanghai Jiao Tong University open-sourced APXInf, an edge inference engine for deploying embodied AI models on robots. It reportedly cuts π0.5 FP8 end-to-end latency on Jetson Thor from 278 ms to under 26 ms, enabling 38.46 Hz real-time inference.
- APXInf targets low-batch, low-latency, resource-constrained robotics workloads through pipeline, graph, kernel, and quantization optimizations.
- The engine uses a minimal Rust runtime for memory safety and predictable operation, with Python interfaces for easier model integration.
- Its initial release supports π0.5 and WALL-OSS on Jetson Orin, Jetson Thor, and RTX 4090 hardware.
- The roadmap includes VLA, VLM, and world models; Qwen and GR00T support; NVFP4 optimization; and additional domestic and AMD hardware backends.