# Announcing apache-tvm-ffi v0.1.0

**URL:** <https://discuss.tvm.apache.org/t/announcing-apache-tvm-ffi-v0-1-0/18696>\
**Category:** Announcements\
**Created:** [October 21, 2025, 9:03pm UTC](https://discuss.tvm.apache.org/t/announcing-apache-tvm-ffi-v0-1-0/18696 "2025-10-21T21:03:23Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![tqchen](https://discuss.tvm.apache.org/user_avatar/discuss.tvm.apache.org/tqchen/32/5_2.png) [@tqchen](https://discuss.tvm.apache.org/u/tqchen)\
**Post date:** [October 21, 2025, 9:03pm UTC](https://discuss.tvm.apache.org/t/announcing-apache-tvm-ffi-v0-1-0/18696/1 "2025-10-21T21:03:24Z")

</div>

Dear Community:

We are excited to announce apache-tvm-ffi v0.1.0

Apache TVM FFI is an open ABI and FFI for ML systems. It is a minimal, framework-agnostic, yet flexible open convention with the following systems in mind:

- Kernel libraries: ship one wheel to support multiple frameworks, Python versions, and different languages.
- Kernel DSLs: reusable open ABI for JIT and AOT kernel exposure to PyTorch, JAX, and other ML runtimes.
- ML frameworks and runtimes: unified mechanism to connect libraries and DSLs that adopt the ABI convention.
- Coding agents: unified mechanism to package and ship generated code to production environments.
- ML infrastructure: cross-language support for Python, C++, and Rust, and DSLs.

It has the following technical features:

- DLPack-compatible Tensor data ABI to seamlessly support many frameworks such as PyTorch, JAX, CuPy and others that support DLPack convention.
- Compact value and function calling convention for common data types in machine learning.
- Stable, minimal, and flexible C ABI to support machine learning system use-cases.
- Out-of-the-box multi-language support for Python, C++, Rust, and future path for other languages.

You can check out the source release here [Index of /tvm/tvm-ffi-v0.1.0](https://downloads.apache.org/tvm/tvm-ffi-v0.1.0/) and also can try out the wheels through apache-tvm-ffi