ArrayFire for AI
Bring accelerated array computing into the language your team already uses.
ArrayFire exposes one native compute core through C++, C, Python, Rust, Julia, Java, .NET, Fortran, Lua, Nim, and Node.js. Prototype in a productive language, move critical paths into native code when needed, and keep the same array model underneath.
Where it fits
ArrayFire is useful where AI systems meet custom numerical work: preprocessing, tensor transforms, signal and image pipelines, model inference support, and native application integration.
Accelerated preparation
Keep normalization, filtering, transforms, feature extraction, and batch preparation close to the device.
Composable tensor operations
Express attention building blocks, reductions, indexing, matrix operations, and elementwise transforms with array primitives.
Native deployment
Embed accelerated computation inside C++ applications without making a Python runtime the center of the production system.
11 Languages, One Accelerated Core
A tiny transformer attention forward pass, expressed through each ArrayFire frontend.
Now AI.
GPT-2 is implemented with ArrayFire in af-gpt2.cc , a standalone, single C++ file. The code undertakes the following steps:
Take the same model from example to deployment.
Install ArrayFire, inspect the complete GPT-2 implementation, or discuss a production AI system with the engineering team.
