Apache TVM vs JAX
Apache TVM
tvm.apache.orgJAX
docs.jax.devSide by side
| Figure | Apache TVM | JAX |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | Yes | — |
| Web | Yes | — |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| iOS | Yes | — |
| Android | Yes | — |
| API | Yes | — |
| Self-hosted | Yes | Yes |
| Plans published | 2 | 1 |
| Spec score · Deep Learning Software | 9.0 (#2) | 8.3 (#7) |
| Installation | Users can install TVM from PyPI, build it from source or use Docker images. | JAX uses the `jax` Python package and `jaxlib`, which contains compiled binaries with different builds for operating systems and accelerators. |
| What it does | Apache TVM compiles pretrained machine-learning models into deployable modules for varied hardware. | JAX is a Python library for accelerator-oriented array computation and program transformation, designed for high-performance numerical computing and large-scale machine learning. |
Plans
In their own words
Apache TVM
Apache TVM is a machine learning compilation framework that turns pre-trained models into deployable modules. Its optimization process can be customized in Python without rebuilding the TVM stack, and users can combine optimization passes, libraries, and code generation. It imports models from PyTorch, ONNX, and TensorFlow Lite. Target backends include CPU, GPU, Metal, ROCm, Vulkan, OpenCL, x86, ARM, and WebAssembly. A lightweight runtime can execute compiled code in JavaScript, Java, Python, and C++ on Android, iOS, Raspberry Pi, and web browsers. Users can install it from PyPI, build from source, or use Docker images; cross-compilation and RPC deployment cover ARM, x86, RISC-V, embedded systems, and accelerator devices. The default generated binary uses a minimal runtime API and limited system calls such as malloc. Apache TVM is free open-source software under Apache License 2.0. Its RPC server assumes trusted users and networks: API users can write arbitrary files and obtain full remote code execution, so it should be treated as a high-trust interface.