Download and install

There are nightly binary builds available. Those builds are not always as stable as the release, but they contain numerous bugfixes and performance improvements.

We provide binaries for x86 and ARM Linux, Mac OS/X and Windows for:

  • the Python2.7 compatible release — PyPy 4.0.1 — (what's new in PyPy 4.0.1?)
  • the Python3.2.5 compatible release — PyPy3 2.4.0 — (what's new in PyPy3 2.4.0?).
  • the Python2.7 Software Transactional Memory special release — PyPy-STM 2.5.1 (Linux x86-64 only)

“JIT Compiler” version

These binaries include a Just-in-Time compiler. They only work on x86 CPUs that have the SSE2 instruction set (most of them do, nowadays), or on x86-64 CPUs. They also contain stackless extensions, like greenlets.

Linux binaries and common distributions

Linux binaries are dynamically linked, as is usual, and thus might not be usable due to the sad story of linux binary compatibility. This means that Linux binaries are only usable on the distributions written next to them unless you're ready to hack your system by adding symlinks to the libraries it tries to open. There are better solutions:

Python2.7 compatible PyPy 4.0.1

Python 3.2.5 compatible PyPy3 2.4.0

Warning: this is (1) based on an old release of PyPy, and (2) only supporting the Python 3.2 language. It's also known to be (sometimes much) slower than PyPy 2.

If your CPU is really, really old, it may be a x86-32 without SSE2. There is untested support for manually translating PyPy's JIT without SSE2 (--jit-backend=x86-without-sse2) but note that your machine is probably low-spec enough that running CPython on it is a better idea in the first place.

[1]: stating it again: the Linux binaries are provided for the distributions listed here. If your distribution is not exactly this one, it won't work, you will probably see: pypy: error while loading shared libraries: …. Unless you want to hack a lot, try out the portable Linux binaries.

PyPy-STM 2.5.1

This is a special version of PyPy! See the Software Transactional Memory (STM) documentation.

Other versions

The other versions of PyPy are:

  • The most up-to-date nightly binary builds with a JIT, if the official release is too old for what you want to do. There are versions for different libc on this site too.
  • Sandboxing: A special safe version. Read the docs about sandboxing. (It is also possible to translate a version that includes both sandboxing and the JIT compiler, although as the JIT is relatively complicated, this reduces a bit the level of confidence we can put in the result.) Note that the sandboxed binary needs a full pypy checkout to work. Consult the sandbox docs for details. (These are old, PyPy 1.8.)


All binary versions are packaged in a tar.bz2 or zip file. When uncompressed, they run in-place. For now you can uncompress them either somewhere in your home directory or, say, in /opt, and if you want, put a symlink from somewhere like /usr/local/bin/pypy to /path/to/pypy-4.0.1/bin/pypy. Do not move or copy the executable pypy outside the tree – put a symlink to it, otherwise it will not find its libraries.

Installing more modules

The recommended way is to install pip, which is the standard package manager of Python. It works like it does on CPython. One practical difference, though, is that it usually comes pre-packaged for you when you get CPython from a place like your Linux distribution. In the case of PyPy (or CPython if you download it from, you need to get it separately, as explained in our FAQ.

Installing NumPy

NumPy is an exception to the rule that most packages work without changes. The “numpy” module needs to be installed from our own repository rather than from the official source.

If you have pip:

pypy -m pip install git+
pypy -m pip install git+

(the second version selects a particular tag, which may be needed if your pypy is not the latest development version.)

Alternatively, the direct way:

git clone
cd numpy
pypy install

If you installed to a system directory, you need to also run this once:

sudo pypy -c 'import numpy'

Note that NumPy support is still a work-in-progress, many things do not work and those that do may not be any faster than NumPy on CPython. For further instructions see the pypy/numpy repository.

Building from source

  1. Get the source code. The following packages contain the source at the same revision as the above binaries:

    Or you can checkout the current trunk using Mercurial (the trunk usually works and is of course more up-to-date):

    hg clone

    The above command may take a long time to run and if it aborts, it is not resumable. You may prefer this way:

    hg clone -r null
    cd pypy
    hg unbundle
    hg unbundle
    hg unbundle
    hg unbundle
    hg unbundle
    hg unbundle
    hg unbundle
    hg unbundle
    hg unbundle
    hg pull
    hg update

    If needed, you can also download the bz2 files by other means. You can then replace the multiple unbundle commands above with a single hg unbundle pypy-bundle-*.bz2.

  2. Make sure you installed the dependencies. See the list here.

  3. Enter the goal directory:

    cd pypy/pypy/goal
  4. Run the rpython script. Here are the common combinations of options (works also with python instead of pypy; requires Python 2.x or PyPy 2):

    pypy ../../rpython/bin/rpython -Ojit targetpypystandalone           # get the JIT version
    pypy ../../rpython/bin/rpython -O2 targetpypystandalone             # get the no-jit version
    pypy ../../rpython/bin/rpython -O2 --sandbox targetpypystandalone   # get the sandbox version
  5. Enjoy Mandelbrot :-) It takes on the order of an hour to finish the translation, and 2.x GB of RAM on a 32-bit system and 4.x GB on 64-bit systems. (Do not start a translation on a machine with insufficient RAM! It will just swap forever. See notes below in that case.)

  6. If you want to install this PyPy as root, please read the next section.


  • It is recommended to use PyPy to do translations, instead of using CPython, because it is twice as fast. You should just start by downloading an official release of PyPy (with the JIT). If you really have to use CPython then note that we are talking about CPython 2.7 here, not CPython 3.x. (CPython 2.6 might or might not work. Older versions are out.)

  • If RAM usage is a problem (or if you are on Windows, because win32's limit is 2 GB unless you have a 64 bit OS), then you can (for now) tweak some parameters via environment variables and command-line options. The following command takes a bit more time, but finishes with only using 3.0 GB of RAM (on Linux 64-bit; probably not much more than 1.6 GB on 32-bit). It should be noted that it is less than with CPython.

    PYPY_GC_MAX_DELTA=200MB pypy --jit loop_longevity=300 ../../rpython/bin/rpython -Ojit targetpypystandalone
  • You can run translations with --source, which only builds the C source files (and prints at the end where). Then you can cd there and execute make. This is another way to reduce memory usage. Note that afterwards, you have to run manually pypy-c .../pypy/tool/ if you want to be able to import the cffi-based modules.

  • On Linux, because of asmgcroot, compiling the generated C files is delicate. It requires using gcc with no particularly fancy options. It does not work e.g. with clang, or if you pass uncommon options with the CFLAGS environment variable. If you insist on passing these options or using clang, then you can compile PyPy with the shadow stack option instead (for some performance price in non-JITted code).

  • Like other JITs, PyPy doesn't work out of the box on some Linux distributions that trade full POSIX compliance for extra security features. E.g. with PAX, you have to run PyPy with paxctl -cm. This also applies to translation (unless you use CPython to run the translation and you specify --source).


Once PyPy is translated from source the binary package similar to those provided in the section Default (with a JIT Compiler) above could be easily created with script as following:

cd ./pypy/pypy/tool/release/
python --help #for information
python --archive-name pypy-my-own-package-name

It is recommended to use because custom scripts will invariably become out-of-date. If you want to write custom scripts anyway, note an easy-to-miss point: some modules are written with CFFI, and require some compilation. If you install PyPy as root without pre-compiling them, normal users will get errors:

  • PyPy 2.5.1 or earlier: normal users would see permission errors. Installers need to run pypy -c “import gdbm” and other similar commands at install time; the exact list is in Users seeing a broken installation of PyPy can fix it after-the-fact if they have sudo rights, by running once e.g. sudo pypy -c "import gdbm.
  • PyPy 2.6 and later: anyone would get ImportError: no module named _gdbm_cffi. Installers need to run pypy in the lib_pypy directory during the installation process (plus others; see the exact list in Users seeing a broken installation of PyPy can fix it after-the-fact, by running pypy /path/to/lib_pypy/ This command produces a file called locally, which is a C extension module for PyPy. You can move it at any place where modules are normally found: e.g. in your project's main directory, or in a directory that you add to the env var PYTHONPATH.


Here are the checksums for each of the downloads

pypy-4.0.1 md5:

f6721e62f4ba1cdc4cc5ad719369e359  pypy-4.0.1-linux64.tar.bz2
fe8106ac3919c7b4be2766944326a624  pypy-4.0.1-linux-armel.tar.bz2
823b8a457f4c48ebdb8e1ee607b0a893  pypy-4.0.1-linux-armhf-raring.tar.bz2
e45728d413aa88963d4462ebcfaff6ea  pypy-4.0.1-linux-armhf-raspbian.tar.bz2
d1d03aa44df354a3f589473a51406795  pypy-4.0.1-linux.tar.bz2
67ac82d88aaaef8c3074e68d700f3968  pypy-4.0.1-osx64.tar.bz2
f5b35ebedee2fa4fdfee82733be59996  pypy-4.0.1-src.tar.bz2
d4492e65201bb09dca5f97601113dc57  pypy-4.0.1-ppc64le.tar.bz2
2aadbb7638153b9d7c2a832888ed3c1e  pypy-4.0.1-ppc64.tar.bz2

pypy3-2.4.0 md5:

eadbc9790823fc0ae40c943087cd7cb3  pypy3-2.4.0-linux64.tar.bz2
7ab84727da2d5363866907f2f7921d86  pypy3-2.4.0-linux-armel.tar.bz2
83158d3a55ca134b179ef01dc2bb6a30  pypy3-2.4.0-linux-armhf-raring.tar.bz2
b0b81cfa46e970c584bda10feebe1a85  pypy3-2.4.0-linux-armhf-raspbian.tar.bz2
68af7a6ca5948a1448a4b9c839d1472c  pypy3-2.4.0-linux.tar.bz2
c6cd12602469446db1dfa1e2bc6c699c  pypy3-2.4.0-osx64.tar.bz2
96ba72916114d16904e12562b5d84e51  pypy3-2.4.0-src.tar.bz2

pypy-1.8 sandbox md5:

2c9f0054f3b93a6473f10be35277825a  pypy-1.8-sandbox-linux64.tar.bz2
009c970b5fa75754ae4c32a5d108a8d4  pypy-1.8-sandbox-linux.tar.bz2

pypy-4.0.1 sha256:

0d6090cee59f4b9bab91ddbea76580d0c232b78dae65aaa9e8fa8d4449ba25b4  pypy-4.0.1-linux64.tar.bz2
d1acdd45ebd34580dd632c63c95211f6bae5e9a8f7a46ffa6f0443286ff9f61b  pypy-4.0.1-linux-armel.tar.bz2
e67278ce7423aa7bf99a95fd271cb76763eae3106930f4b7de1fba6a70a3f383  pypy-4.0.1-linux-armhf-raring.tar.bz2
52eef495f560af59a787b9935367cb5f8c83b48e32a80ec3e7060bffac011ecc  pypy-4.0.1-linux-armhf-raspbian.tar.bz2
721920fcbb6aefc9a98e868e32b7f4ea5fd68b7f9305d08d0a2595327c9c0611  pypy-4.0.1-linux.tar.bz2
06be1299691f7ea558bf8e3bdf3d20debb8ba03cd7cadf04f2d6cbd5fd084430  pypy-4.0.1-osx64.tar.bz2
43be0b04bcbde1e24d5f39875c0471cdc7bdb44549e5618d32e49bccaa778111  pypy-4.0.1-ppc64le.tar.bz2
63c0a1614ffc94f94a64790df5ad193193b378f2cf8729213db06fbd64052911  pypy-4.0.1-ppc64.tar.bz2
29f5aa6ba17b34fd980e85172dfeb4086fdc373ad392b1feff2677d2d8aea23c  pypy-4.0.1-src.tar.bz2

pypy3-2.4.0 sha1:

7d715742f6929351b310a2ca3b924cab35913089  pypy3-2.4.0-linux64.tar.bz2
b33e817f3557f91c434032c9f74e5220fe70036c  pypy3-2.4.0-linux-armel.tar.bz2
bb098b72ecc83a0e73c426f364bb6a0974fb9360  pypy3-2.4.0-linux-armhf-raring.tar.bz2
775dc9f8073c4fad7cd220c4b5dd385e7be469e9  pypy3-2.4.0-linux-armhf-raspbian.tar.bz2
c39061f3e5e7a05548eb89c5cbd3ed81a795879f  pypy3-2.4.0-linux.tar.bz2
9f01d8c5e18c8c7d54fc6ab77dbf5673a65c2af9  pypy3-2.4.0-osx64.tar.bz2
438572443ae6f54eb6122d807f104787c5247e01  pypy3-2.4.0-src.tar.bz2

pypy-1.8 sandbox sha1:

895aaf7bba5787dd30adda5cc0e0e7fc297c0ca7  pypy-1.8-sandbox-linux64.tar.bz2
be94460bed8b2682880495435c309b6611ae2c31  pypy-1.8-sandbox-linux.tar.bz2