What Actually Happens When You Disable the GIL (Spoiler: Everything Breaks)

The biggest change to CPython in decades happened with PEP 703 - they finally made the GIL optional. This should have been exciting, except now your single-threaded code runs like molasses and your C extensions crash with mysterious errors.

The Global Interpreter Lock has been Python's achilles heel since the beginning, forcing true parallelism through multiprocessing hell. The experimental free-threaded mode finally offers an alternative, but at a steep cost.

The Reality Check Nobody Talks About

Yeah, you can finally run Python threads in parallel. Built-in types like dict and list got internal locking so they won't corrupt themselves. Sounds great until you realize the 40% performance hit on single-threaded code because they had to disable the adaptive interpreter (PEP 659).

The CodSpeed benchmarks paint a brutal picture - single-threaded code gets hammered while multithreaded gains remain modest. Python 3.14 might fix this, but that's at least a year away.

Here's what actually happens when you install this thing:

Installation Hell: Choose Your Poison

Official Python.org installers work fine if you don't mind your PATH getting fucked up. Windows adds entries that conflict with existing Python installations, and macOS loves to hide the python3.13t binary somewhere deep in /Library/Frameworks.

Package managers are hit-or-miss. Homebrew is solid but slow. The Ubuntu Deadsnakes PPA breaks every other month when maintainers get busy. Conda takes forever but at least isolates everything properly.

Building from source is masochism unless you enjoy spending hours debugging compiler errors. You'll need every dev tool installed and patience for the 30-minute build process on older machines.

System Requirements (AKA What Will Break)

You need a modern OS, at least 2GB of RAM (despite what the docs claim), and the willingness to nuke your Python environment when things inevitably go wrong.

ARM64 Macs work surprisingly well, but x86_64 Windows machines love to throw `VCRUNTIME140.dll` errors that make no fucking sense. Linux is the most stable option, which says something about the state of this feature.

GitHub Python CPython Free Threading Issues

Before You Waste Hours Installing This

Q

Should I use this in production?

A

Hell no. It's marked experimental for good reason. I've seen it crash randomly under load, segfault when importing certain packages, and mysteriously slow down overnight when the GIL re-enables itself. PEP 779 has a wishlist for making it stable, but that's years away.

Q

Will this break my existing code?

A

Your code might run, but prepare for weird bugs. Simple Python code usually works fine until you import something with C extensions. Then you get delightful errors like SystemError: PyEval_SaveThread: NULL tstate with zero helpful context.

C extensions will break spectacularly and the error messages are useless. Package compatibility is hit-or-miss - check py-free-threading.github.io/tracking before you commit to this.

Q

How slow is "40% slower"?

A

Your single-threaded code will feel sluggish. I tested a simple web scraper that normally runs in 30 seconds - with free-threading it took 45 seconds. The 40% performance hit is real and noticeable.

The multithreaded speedups are legit though. CPU-bound parallel work can see 2-3x improvements, but only if your code actually parallelizes well and doesn't hit memory contention issues.

Q

Can I keep my normal Python too?

A

Yes, and you absolutely should. Free-threaded Python installs as python3.13t so it won't replace your working interpreter. Your normal python3 will still work, which is good because you'll need it when this experimental shit breaks.

Q

Do I need to reinstall all my packages?

A

Everything needs to be reinstalled with compatible cp313t wheels. NumPy works now, but SciPy is broken as hell. TensorFlow doesn't exist yet for free-threading. Pandas sometimes works, sometimes doesn't.

The kicker? Even if a package installs successfully, it might crash at runtime or silently produce wrong results. Welcome to experimental software!

Still want to proceed? The installation methods below will show you exactly what you're signing up for.

Installation Methods That Actually Work (Sometimes)

Windows Installation - Pick Your Struggle

NuGet: Works Great When PowerShell Doesn't Hate You

The NuGet approach is clean but loves to fail with execution policy errors. The python-freethreading package updates regularly but PowerShell security gets in the way:

## This will fail if you have spaces in your username - fucking Windows
$url = 'https://www.nuget.org/api/v2/package/python-freethreaded/3.13.1'
Invoke-WebRequest -Uri $url -OutFile 'python-freethreaded.3.13.1.nupkg'

## PowerShell will probably block this next line
Install-Package python-freethreaded -Scope CurrentUser -Source $pwd

## If you get \"execution policy\" errors, run this first:
## Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser

$python_dir = (Get-Item((Get-Package -Name python-freethreaded).Source)).DirectoryName
$env:path = $python_dir + \"	ools;\" + $python_dir + \"	ools\Scripts;\" + $env:Path

Official Installer: Fine Until Your PATH Gets Fucked

Download from python.org/downloads but beware - the Windows installer quirks will bite you:

  1. Click "Customize installation"
  2. Check "Download free-threaded binaries (experimental)"
  3. Pray it doesn't conflict with existing Python installations

The installer loves to add duplicate PATH entries that break everything. You'll find python3.13t.exe buried somewhere in C:\Users\[username]\AppData\Local\Programs\Python\Python313\ instead of where you expect it.

Windows Python Installation Dialog

macOS Installation - Less Broken Than Windows

Official Installer: Actually Works Most of the Time

  1. Download from python.org
  2. Run the installer
  3. Click "Customize" and check "Free-threaded Python [experimental]"
  4. Hope it doesn't corrupt your existing Python installation

The installer sometimes hides python3.13t in /Library/Frameworks/Python.framework/Versions/3.13/bin/ instead of putting it in /usr/local/bin like a sane person would expect. You might need to add that to your PATH manually.

Command Line Installation for Masochists:

## This works if you enjoy XML hell
curl -O https://www.python.org/ftp/python/3.13.3/python-3.13.3-macos11.pkg

## Creating XML configs by hand in 2025 - peak developer experience
cat > ./choicechanges.plist <<EOF
<?xml version=\"1.0\" encoding=\"UTF-8\"?>
<!DOCTYPE plist PUBLIC \"-//Apple//DTD PLIST 1.0//EN\" \"http://www.apple.com/DTDs/PropertyList-1.0.dtd\">
<plist version=\"1.0\">
<array>
        <dict>
                <key>attributeSetting</key>
                <integer>1</integer>
                <key>choiceAttribute</key>
                <string>selected</string>
                <key>choiceIdentifier</key>
                <string>org.python.Python.PythonTFramework-3.13</string>
        </dict>
</array>
</plist>
EOF

## This will probably ask for your password 3 times
sudo installer -pkg ./python-3.13.3-macos11.pkg \
    -applyChoiceChangesXML ./choicechanges.plist \
    -target /

Homebrew: Slow But Reliable

## Takes forever but at least it works
brew install python-freethreading

You'll find it at $(brew --prefix)/bin/python3.13t - one of the few things Homebrew puts in a sensible location. Check the formula page for the latest version.

Homebrew Installation Terminal

Linux Installation - The Least Painful Option

Fedora: Just Works

sudo dnf install python3.13-freethreading
## Actually works as expected - shocking for an experimental feature

Ubuntu/Debian: Russian Roulette with PPAs

## The Deadsnakes PPA breaks every other month when maintainers get busy
sudo add-apt-repository ppa:deadsnakes/ppa  # Note: needs /ppa suffix
sudo apt-get update
sudo apt-get install python3.13-nogil

## If you get \"package not found\", the PPA is probably broken again
## Check https://launchpad.net/~deadsnakes/+archive/ubuntu/ppa

The Deadsnakes PPA is maintained by volunteers who do amazing work, but experimental builds like free-threading often lag behind. Check the PPA status before trying.

Arch Linux: Surprisingly Stable

## AUR packages are hit-or-miss but this one usually works
yay -S python313-freethreading

NixOS: For People Who Like Complexity

## Works perfectly in a pristine environment that's impossible to reproduce
nix shell nixpkgs#python313FreeThreading

Package Managers: Choose Your Disappointment

Conda: Slow But Thorough

## Takes 5 minutes to solve the environment, but at least it's isolated
mamba create -n freethreaded -c conda-forge python-freethreading
## mamba is faster than conda - use it if you have it

uv: The New Hotness That Actually Works

## Modern Python tooling that doesn't suck - rare
uv venv --python 3.13t myproject
source myproject/bin/activate
## This is probably your best bet for a clean installation

uv is the fastest Python package manager and handles free-threaded Python better than pip. It's written in Rust and doesn't make you wait 5 minutes to solve dependencies like conda does.

Building from Source: For People Who Enjoy Pain

## Prepare for 30 minutes of compiler warnings and cryptic errors
git clone https://github.com/python/cpython.git
cd cpython

## You'll need build-essential, libssl-dev, libffi-dev, and 20 other packages
./configure --disable-gil --prefix=/usr/local/python3.13t

## Make sure you have -j$(nproc) or this will take forever
make -j$(nproc)  # Pray your system doesn't run out of RAM
sudo make install

## If you get \"No rule to make target\" errors, you're missing dependencies
## If it builds but segfaults, you probably have conflicting libraries

Building from source is only worth it if you're debugging CPython itself or need bleeding-edge features. The CPython developer guide has all the gory details, but for everyone else, use a package manager like a sane person.

CPython GitHub Repository

Too many options? The comparison below cuts through the marketing bullshit and tells you what actually works.

What Actually Works: Honest Installation Recommendations

Method

Tier

Description

Time Estimate

uv

The Winners

Modern Python tooling that doesn't suck. Fast installs, clean isolation, and it usually works the first time. If you're starting fresh, use this.uv GitHub Repository

2 minutes if lucky, 10 minutes if unlucky

Homebrew (macOS)

The Winners

Slow as molasses but rock solid. It'll take 10 minutes to install but you won't spend hours debugging broken PATH entries.

10 minutes guaranteed, no surprises

Fedora DNF

The Winners

The only Linux package manager that consistently has working free-threaded packages. Other distros are hit-or-miss.

Official Python.org installers

The "Eh, It's Fine" Tier

Work fine if you don't mind manual PATH fixing on Windows. macOS version is surprisingly decent.

5 minutes + 20 minutes fixing PATH issues

Conda/Mamba

The "Eh, It's Fine" Tier

Takes forever to solve environments but at least it's properly isolated. Good if you're already in the conda ecosystem.Conda Forge GitHub

15 minutes solving, 5 minutes installing

Ubuntu Deadsnakes PPA

The "Avoid Unless You're Desperate" Tier

Breaks every few months when maintainers get busy. Works great when it's not broken, which is maybe 70% of the time.

Windows NuGet

The "Avoid Unless You're Desperate" Tier

Clean when it works, but PowerShell execution policies will fuck you over. Only use if you understand Windows security theater.

Building from source

The "Don't Do This to Yourself" Tier

Only worth it if you're debugging CPython itself. Prepare for dependency hell and 30+ minute build times.

2 hours if you're missing dependencies, 30 minutes if you're not

Docker containers

The "Don't Do This to Yourself" Tier

Adds unnecessary complexity for local development. Save containers for production deployment.

When Free-Threaded Python Breaks (Spoiler: It Will)

Q

How do I know if this thing even installed correctly?

A

Copy this and pray it doesn't crash:

## Basic sanity check - if this fails, start over
python3.13t -VV
## Should show: Python 3.13.x (... experimental free-threading build)

## The real test - does the GIL actually fuck off?
python3.13t -c "import sys; print('GIL enabled:', sys._is_gil_enabled())"
## Should show: GIL enabled: False (if it shows True, you're fucked)
Q

My GIL keeps magically re-enabling itself

A

Welcome to experimental software hell. The GIL auto-enables itself when it encounters old C extensions. Sometimes you get a warning, sometimes you don't.

Force disable it and watch things break spectacularly:

## Nuclear option - disables GIL even when extensions demand it
PYTHON_GIL=0 python3.13t your_script.py

## This will probably segfault or give you corrupted data
## But hey, the GIL is disabled!
Q

"ERROR: No matching distribution found" - the wheel hell begins

A

Half your packages don't have cp313t wheels because maintainers haven't bothered yet. Your options are all terrible:

## The stupid solution that works sometimes
python3.13t -m pip install package_name --no-binary=package_name

## This will fail if the package has complex C dependencies
## Prepare for "error: Microsoft Visual C++ 14.0 is required" on Windows
## Or "fatal error: 'Python.h' not found" on Linux

Check py-free-threading.github.io/tracking but half the status info is outdated.

Q

ImportError hell: "compiled against different Python"

A

This gem appears when you mix packages from regular Python and free-threaded Python:

ImportError: /site-packages/numpy/core/_multiarray_umath.cpython-313-x86_64-linux-gnu.so was compiled against a different Python

The nuclear fix:

## Nuke everything and start over - the Python way
rm -rf venv/
rm -rf __pycache__/
rm -rf .pytest_cache/
python3.13t -m venv venv
source venv/bin/activate
pip install -r requirements.txt  # Cross your fingers
Q

My performance is shit - what went wrong?

A

Everything. Here's your debugging checklist:

import sys, threading, os

print(f"GIL enabled: {sys._is_gil_enabled()}")  # If True, game over
print(f"Thread count: {threading.active_count()}")  # Should be > 1
print(f"CPU cores: {os.cpu_count()}")

## If GIL is True but you expected False, some C extension re-enabled it
## Check which packages you imported - probably NumPy or SciPy

Common performance killers:

  • Single-threaded code (40% slower, nothing you can do)
  • Memory thrashing from immortalized objects eating RAM
  • The GIL randomly re-enabling itself
  • Your threading code sucks (most likely)
Q

Virtual environments: surprisingly not broken

A

This actually works most of the time:

## Standard venv - works fine
python3.13t -m venv myproject
source myproject/bin/activate

## uv is probably your best bet
uv venv --python 3.13t myproject

Pro tip: Don't try to upgrade existing venvs to free-threaded. Nuke them and start fresh or you'll spend hours debugging weird import errors.

Resources That Might Actually Help (Don't Get Your Hopes Up)

Related Tools & Recommendations

integration
Similar content

Dask for Large Datasets: When Pandas Crashes & How to Scale

Your 32GB laptop just died trying to read that 50GB CSV. Here's what to do next.

pandas
/integration/pandas-dask/large-dataset-processing
100%
howto
Similar content

Install Node.js & NVM on Mac M1/M2/M3: A Complete Guide

My M1 Mac setup broke at 2am before a deployment. Here's how I fixed it so you don't have to suffer.

Node Version Manager (NVM)
/howto/install-nodejs-nvm-mac-m1/complete-installation-guide
96%
integration
Similar content

Claude API Node.js Express: Advanced Code Execution & Tools Guide

Build production-ready applications with Claude's code execution and file processing tools

Claude API
/integration/claude-api-nodejs-express/advanced-tools-integration
83%
tool
Recommended

Django - The Web Framework for Perfectionists with Deadlines

Build robust, scalable web applications rapidly with Python's most comprehensive framework

Django
/tool/django/overview
58%
tool
Recommended

Django Troubleshooting Guide - Fixing Production Disasters at 3 AM

Stop Django apps from breaking and learn how to debug when they do

Django
/tool/django/troubleshooting-guide
58%
howto
Recommended

Deploy Django with Docker Compose - Complete Production Guide

End the deployment nightmare: From broken containers to bulletproof production deployments that actually work

Django
/howto/deploy-django-docker-compose/complete-production-deployment-guide
58%
tool
Recommended

FastAPI Production Deployment - What Actually Works

Stop Your FastAPI App from Crashing Under Load

FastAPI
/tool/fastapi/production-deployment
58%
tool
Recommended

FastAPI - High-Performance Python API Framework

The Modern Web Framework That Doesn't Make You Choose Between Speed and Developer Sanity

FastAPI
/tool/fastapi/overview
58%
integration
Recommended

Claude API + FastAPI Integration: The Real Implementation Guide

I spent three weekends getting Claude to talk to FastAPI without losing my sanity. Here's what actually works.

Claude API
/integration/claude-api-fastapi/complete-implementation-guide
58%
tool
Recommended

pandas - The Excel Killer for Python Developers

Data manipulation that doesn't make you want to quit programming

pandas
/tool/pandas/overview
55%
tool
Recommended

Fixing pandas Performance Disasters - Production Troubleshooting Guide

When your pandas code crashes production at 3AM and you need solutions that actually work

pandas
/tool/pandas/performance-troubleshooting
55%
compare
Recommended

Python vs JavaScript vs Go vs Rust - Production Reality Check

What Actually Happens When You Ship Code With These Languages

rust
/compare/python-javascript-go-rust/production-reality-check
50%
tool
Recommended

Snyk Container - Because Finding CVEs After Deployment Sucks

Container security that doesn't make you want to quit your job. Scans your Docker images for the million ways they can get you pwned.

Snyk Container
/tool/snyk-container/overview
40%
news
Recommended

Builder.ai's $1.5B AI Fraud Exposed: "AI" Was 700 Human Engineers

Microsoft-backed startup collapses after investigators discover the "revolutionary AI" was just outsourced developers in India

OpenAI ChatGPT/GPT Models
/news/2025-09-01/builder-ai-collapse
40%
news
Recommended

ISRO Built Their Own Processor (And It's Actually Smart)

India's space agency designed the Vikram 3201 to tell chip sanctions to fuck off

c
/news/2025-09-03/isro-vikram-processor
40%
howto
Similar content

Install GitHub CLI: A Step-by-Step Setup Guide

Tired of alt-tabbing between terminal and GitHub? Get gh working so you can stop clicking through web interfaces

GitHub CLI
/howto/github-cli-install/complete-setup-guide
40%
integration
Similar content

Claude API Node.js Express Integration: Complete Guide

Stop fucking around with tutorials that don't work in production

Claude API
/integration/claude-api-nodejs-express/complete-implementation-guide
40%
tool
Similar content

Node.js Performance Optimization: Boost App Speed & Scale

Master Node.js performance optimization techniques. Learn to speed up your V8 engine, effectively use clustering & worker threads, and scale your applications e

Node.js
/tool/node.js/performance-optimization
38%
howto
Similar content

API Rate Limiting: Complete Implementation Guide & Best Practices

Because your servers have better things to do than serve malicious bots all day

Redis
/howto/implement-api-rate-limiting/complete-setup-guide
35%
integration
Similar content

Redis Caching in Django: Boost Performance & Solve Problems

Learn how to integrate Redis caching with Django to drastically improve app performance. This guide covers installation, common pitfalls, and troubleshooting me

Redis
/integration/redis-django/redis-django-cache-integration
35%

Recommendations combine user behavior, content similarity, research intelligence, and SEO optimization