Writing GPU-Ready AI Models in Pure Java with Babylon

Imagine building AI models like LLMs, image classifiers, or speech recognizers, directly in Java, and running them fast on your GPU.
Project Babylon introduces the experimental Code Reflection technology that lets you define machine learning logic in plain Java code, without needing Python or external model files. It then uses Foreign Function and Memory (FFM) API to connect your code to native runtimes like ONNX Runtime for fast inference, including GPU acceleration. Furthermore, the Heterogeneous Accelerator Toolkit (HAT) provides a developer-facing programming model for writing and composing compute kernels, which can be more broadly applied-allowing Java libraries to seamlessly harness GPU power for high-performance computing tasks.
In this session, we’ll introduce you to Babylon’s upcoming features, and how they bridge the worlds of Java and modern AI workloads. Whether you’re curious about new Java features or looking for practical ways to bring AI into your Java stack, join us for a first look at Babylon’s vision for Java and machine learning.