Compile a PyTorch or JAX model with iree-base-compiler and deploy the result to a mobile device or embedded system.
Benchmark AI inference performance across different hardware backends (GPU, CPU, Vulkan) using the same compiled model.
Integrate IREE into a production pipeline to run the same trained model on data center GPUs and edge devices without rewriting inference code.
| iree-org/iree | google/fuzzing | kiwibrowser/src.next | |
|---|---|---|---|
| Stars | 3,759 | 3,760 | 3,762 |
| Language | C++ | C++ | C++ |
| Setup difficulty | hard | easy | hard |
| Complexity | 5/5 | 1/5 | 5/5 |
| Audience | researcher | developer | developer |
Figures from each repo's GitHub metadata at analysis time.
Python packages are on PyPI for quick start, full hardware-specific targets (CUDA, ROCm) require matching drivers and toolchains on the host machine.
IREE (Intermediate Representation Execution Environment, pronounced "eerie") is a compiler and runtime toolkit for machine learning models. Its job is to take a trained AI model, written using frameworks like PyTorch, JAX, or ONNX, and compile it into an efficient form that can run on a specific piece of hardware. The same model can be compiled to run on a data center GPU, a laptop, a phone, or an embedded device, which is what the project means when it calls itself "retargetable." Under the hood, IREE is built on top of MLIR, a compiler infrastructure developed as part of the LLVM project that makes it easier to build compilers for multiple hardware targets. IREE takes the ML model, lowers it through a series of intermediate representations, and produces code tuned for the target device. Supported hardware backends include NVIDIA GPUs via CUDA, AMD GPUs via ROCm, cross-platform GPU access via Vulkan and Metal, and standard CPUs. The project is used in real deployments. In April 2025, AMD submitted an IREE-based image generation implementation to the MLPerf benchmark suite, a standard industry benchmark for AI inference performance. IREE is also a member of the Linux Foundation AI and Data Foundation. For developers, IREE is available as two Python packages on PyPI: iree-base-compiler for the compilation step and iree-base-runtime for running the compiled output. The project is licensed under Apache 2.0 with LLVM Exceptions, and active development discussions happen on a Discord server and mailing lists.
IREE is a compiler and runtime that takes a trained AI model (PyTorch, JAX, ONNX) and compiles it to run efficiently on any hardware, GPU, phone, or embedded device, from a single codebase.
Mainly C++. The stack also includes C++, Python, MLIR.
Apache 2.0 with LLVM Exceptions, use freely for any purpose including commercial, as long as you keep the copyright notice, the LLVM Exception permits linking without copyleft requirements.
Setup difficulty is rated hard, with roughly 1h+ to a first successful run.
Mainly researcher.
This repo across BitVibe Labs
Don't trust strangers blindly. Verify against the repo.