apache-tvm-ffi
tvm ffi
Decision gist · record as of 2026-08-14
Yes, if you are building or consuming ML kernel libraries or DSLs that need to work across multiple frameworks. The stable C ABI, zero-copy interop, and multi-language support make it a solid choice for infrastructure teams. However, be aware that the project is in RFC stage and may evolve; if you require absolute API stability, wait for the first semantic-versioning release. No known security vulnerabilities.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >= 3.9; pre-built wheels available for Python 3.10–3.14 on Linux (manylinux), macOS (arm64), and Windows; other configurations may require building from source.
- Medium install friction due to platform-specific wheels for multiple Python versions (3.10–3.14) and architectures (x86_64, aarch64, arm64, Windows).
- Active maintenance with a release 4 days old and recent commits; early-stage RFC status means the ABI is stable but the project is still soliciting community feedback.
License · maintenance · safety
Apache 2.0 (permissive) — Apache 2.0 permissive license allows use in most commercial and open-source projects without significant restriction, though derivative works must include license notices.
last release 2026-08-10 (4 days) · last repo commit 2026-08-14 · 445 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 7,843,396 downloads/mo, #1,689 on PyPI
Alternatives
Verify before relying
pip install apache-tvm-ffi
import tvm.ffi
# Use TVM FFI to expose or consume ABI-compliant kernels across frameworks- Whether the RFC stage will introduce breaking C ABI changes before semantic versioning begins
- Performance overhead of the FFI layer compared to direct framework-specific APIs
- Completeness of multi-language support (C++, Rust) in the current release
What it is and what it does
Apache TVM FFI is an open ABI and FFI standard designed to let machine learning libraries, kernel DSLs, and runtimes interoperate across frameworks without recompilation. It defines a stable C ABI for exposing kernels and extensions, and provides Python bindings with zero-copy tensor interchange via the DLPack protocol. The package enables a single compiled wheel to work across PyTorch, JAX, CuPy, and NumPy, and supports multi-language bindings (Python, C++, Rust). It is currently in RFC status, meaning the core ABI is stable but the project is actively gathering community feedback before moving to semantic versioning.
The package depends only on typing-extensions and is available as pre-built wheels for modern Python versions (3.10–3.14) across Linux, macOS, and Windows. It is intended for kernel library authors, DSL framework developers, and ML infrastructure teams who need a framework-agnostic way to ship optimized code and integrate with multiple downstream consumers.
Use it for
- Ship a single compiled kernel library wheel that works with PyTorch, JAX, and CuPy without rebuilding
- Expose a custom kernel DSL (like TileLang or cuteDSL) through a stable ABI for consumption by multiple frameworks
- Build ML infrastructure that accepts ABI-compliant extensions from third-party kernel libraries
- Enable zero-copy tensor passing between PyTorch and JAX using DLPack without data copying
- Integrate generated code from coding agents into production ML systems via a unified ABI
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building or consuming ML kernel libraries or DSLs that need to work across multiple frameworks.
The stable C ABI, zero-copy interop, and multi-language support make it a solid choice for infrastructure teams. However, be aware that the project is in RFC stage and may evolve; if you require absolute API stability, wait for the first semantic-versioning release. No known security vulnerabilities.
Install
apache-tvm-ffi on PyPI
Before you install
Medium install friction due to platform-specific wheels for multiple Python versions (3.10–3.14) and architectures (x86_64, aarch64, arm64, Windows). Active maintenance with a release 4 days old and recent commits; early-stage RFC status means the ABI is stable but the project is still soliciting community feedback.
Requires Python >= 3.9; pre-built wheels available for Python 3.10–3.14 on Linux (manylinux), macOS (arm64), and Windows; other configurations may require building from source.
License in practice
Apache 2.0 permissive license allows use in most commercial and open-source projects without significant restriction, though derivative works must include license notices.
Quickstart
pip install apache-tvm-ffi
import tvm.ffi
# Use TVM FFI to expose or consume ABI-compliant kernels across frameworksVerify before relying
- Whether the RFC stage will introduce breaking C ABI changes before semantic versioning begins
- Performance overhead of the FFI layer compared to direct framework-specific APIs
- Completeness of multi-language support (C++, Rust) in the current release
Package facts
| License | Apache 2.0 permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 1 packagetyping-extensions |
| Maintenance | Actively maintained 4 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 7,843,396 / month, #1,689 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software License |
Evidence: apache_tvm_ffi-0.1.13.post3-cp310-cp310-macosx_11_0_arm64.whl; apache_tvm_ffi-0.1.13.post3-cp310-cp310-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; apache_tvm_ffi-0.1.13.post3-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; apache_tvm_ffi-0.1.13.post3-cp310-cp310-win_amd64.whl; apache_tvm_ffi-0.1.13.post3-cp311-cp311-macosx_11_0_arm64.whl; apache_tvm_ffi-0.1.13.post3-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; apache_tvm_ffi-0.1.13.post3-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; apache_tvm_ffi-0.1.13.post3-cp311-cp311-win_amd64.whl; apache_tvm_ffi-0.1.13.post3-cp312-cp312-macosx_11_0_arm64.whl; apache_tvm_ffi-0.1.13.post3-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; apache_tvm_ffi-0.1.13.post3-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; apache_tvm_ffi-0.1.13.post3-cp312-cp312-win_amd64.whl; apache_tvm_ffi-0.1.13.post3-cp313-cp313-macosx_11_0_arm64.whl; apache_tvm_ffi-0.1.13.post3-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; apache_tvm_ffi-0.1.13.post3-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; apache_tvm_ffi-0.1.13.post3-cp313-cp313-win_amd64.whl; apache_tvm_ffi-0.1.13.post3-cp314-cp314-macosx_11_0_arm64.whl; apache_tvm_ffi-0.1.13.post3-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; apache_tvm_ffi-0.1.13.post3-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; apache_tvm_ffi-0.1.13.post3-cp314-cp314t-macosx_11_0_arm64.whl
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