Documentation

AOT vs JIT for Python: An Honest Comparison

Warm-up, peak performance, compatibility and running cost: the AOT vs JIT trade-offs for Python, the cases where each wins, and Pyvorin's position.

8 min read

Debugging Compiled Code

How to debug code compiled by Pyvorin Native 1.0.9: the support, explain and inspect commands, fallback reason chains, and cache forensics.

How to Handle Compilation Failure

Compilation failure in pyvorin-native 1.0.9 almost never means a crash: the verified taxonomy, what each status means, and how to get per-function reasons.

Migrating from Numba to Pyvorin

What @njit gave you, what Pyvorin compilation gives you instead, which kernels should stay on Numba, and how to verify each migrated function.

Pyvorin vs Codon

Codon compiles Python-syntax code to standalone native binaries; Pyvorin compiles the Python you already run, in-process, beside CPython. Where each fits.

Pyvorin vs CPython

Pyvorin runs your existing Python as native machine code beside CPython. Where that helps, where it does not, and the measured results from 71 workloads.

Pyvorin vs Mojo

Mojo and Pyvorin both promise Python-like ergonomics with native performance, but they answer different questions. Goals, fit, and honest trade-offs.

Unsupported Code and the Fallback Path

When Pyvorin cannot take a function native, your code still runs — via a recorded, per-call fallback to lazily compiled CPython. Here is exactly how that works.