When you pip install a package with compiled code, the wheel you get is built for CPU features from 2009. Want newer optimizations like AVX2? Your installer has no way to ask for them. GPU support? You're on your own configuring special index URLs. The result is fat binaries, nearly gigabyte-sized wheels, and install pages that read like puzzle books. A coalition from NVIDIA, Astral, and QuanSight has been working on Wheel Next: A set of PEPs that let packages declare what hardware they need and let installers like uv pick the right build automatically. Just uv pip install torch and it works. I sit down with Jonathan Dekhtiar from NVIDIA, Ralf Gommers from Quansight and the NumPy and SciPy teams, and Charlie Marsh, founder of Astral and creator of uv, to dig into all of it.

Sentry Error Monitoring, Code talkpython26

Temporal

Talk Python Courses

Links from the show

Charlie Marsh: github.com

Ralf Gommers: github.com

Jonathan Dekhtiar: github.com

CPU dispatcher: numpy.org

build options: numpy.org

Red Hat RHEL: www.redhat.com

Red Hat RHEL AI: www.redhat.com

RedHats presentation: wheelnext.dev

CUDA release: developer.nvidia.com

requires a PEP: discuss.python.org

WheelNext: wheelnext.dev

Github repo: github.com

PEP 817: peps.python.org

PEP 825: discuss.python.org

uv: docs.astral.sh

A variant-enabled build of uv: astral.sh

pyx: astral.sh

pypackaging-native: pypackaging-native.github.io

PEP 784: peps.python.org

Watch this episode on YouTube: youtube.com

Episode #544 deep-dive: talkpython.fm/544

Episode transcripts: talkpython.fm

🥁 Served in a Flask 🎸: talkpython.fm/flasksong

YouTube: youtube.com/@talkpython

Bluesky: @talkpython.fm

Mastodon: @[email protected]

X.com: @talkpython

Michael on Bluesky: @mkennedy.codes

Michael on Mastodon: @[email protected]

Michael on X.com: @mkennedy