CuPy vs Taichi in 2026

Q: Is PyOverdrive free to use?

PyOverdrive is open source under the MIT License . No price is stated for it, and its status is Beta . The package needs Python 3.12 or newer . Read the MIT License text itself for the terms that apply to how you use the code, since this page does not restate them.

CuPy and Taichi compared on what each is built for, how each installs, what pricing is published, and what to check before you choose one.

By SayDeploy · 2026-10-28

SayDeploy is published by LunarWerx, which makes some of the tools compared here.

CuPy is an open-source array library for GPU computing with Python ^[1]. Taichi's just-in-time compiler turns Python functions into GPU or CPU machine code for parallel runs ^[2]. PyOverdrive takes a different route: it changes how supported NumPy calls run in code you already have ^[3].

Option	What it does	Price	Install
CuPy	Open-source array library for GPU computing with Python ^[1]	Not published	Precompiled packages (wheels) for Linux and Windows ^[1]
Taichi	A language embedded in Python for writing portable, high-performance parallel programs ^[2]	Not published	pip install taichi -U ^[2]
PyOverdrive	Makes supported NumPy calls use faster routines, and calls it does not support run on stock NumPy ^[3]	No price stated; open source under the MIT License ^[3]	pip, straight from the GitHub repository ^[3]

The short answer

CuPy is an open-source array library for GPU computing with Python ^[1]. Its page says that in most cases it can be used as a drop-in replacement for NumPy and SciPy, the Python number libraries ^[1]. Taichi's just-in-time compiler turns Python functions into machine code that runs on a GPU or CPU in parallel ^[2]. The need its sources speak to for CuPy is running NumPy and SciPy style array code on a graphics processor ^[1], and for Taichi it is writing Python functions that run in parallel on a GPU or CPU ^[2]. PyOverdrive addresses a different need, which is making NumPy code you already have run on faster routines ^[3].

What CuPy and Taichi are built for

CuPy's page describes it as an open-source array library for GPU computing with Python ^[1]. The page says that in most cases it can be used as a drop-in replacement for NumPy and SciPy ^[1]. The facts available for this page say nothing more about its features, so none are described here.

Taichi's just-in-time compiler automatically compiles Python functions into fast GPU or CPU machine code for parallel execution ^[2]. Its page says it has over 200 contributors from all over the world ^[2]. The facts for this page do not describe Taichi's features beyond these points, so it is not described further here.

What PyOverdrive is built for

PyOverdrive is made by the team that publishes this site. Calling pyoverdrive.enable() patches supported NumPy functions so your existing code uses faster routines, and calls it does not support run on stock NumPy ^[3]. Before a faster routine runs, a check confirms the input is in its measured range, and anything else, or a faster routine that raises an error, goes to stock NumPy ^[3]. For np.sin, np.cos, np.tan, np.exp, np.log, np.log10 and np.tanh on arrays of 64-bit or 32-bit decimal numbers, routines that split the work across CPU threads run above a size set by measurement for each operation and data type ^[3]. Calls to np.linalg.qr on large stacks of 3x3 matrices use a faster direct formula, and np.linalg.eigvalsh has direct formulas for 2x2 and 3x3 batches above a size threshold ^[3]. Large np.einsum calls are sent through NumPy's own optimize=True planner once they pass a measured size threshold ^[3]. np.isin on one-dimensional StringDType or object arrays can use a lookup method built on hashing in place of the default method ^[3]. Running python -m pyoverdrive --selfcheck compares every faster routine with stock NumPy on your own machine ^[3]. Running python -m pyoverdrive --calibrate re-times the threaded routines at their own thresholds on your CPU and switches off any that do not pay off there ^[3]. Calling pyoverdrive.explain() tells you which routine a call would take and why, without running the call ^[3]. You can turn off one faster routine with disable_path() or the PYOVERDRIVE_DISABLE setting, or call pyoverdrive.disable() to restore the original NumPy functions ^[3].

Pricing and fees

Neither the CuPy sources nor the Taichi sources given here state a price, so no price is published here for either tool. CuPy is described as open source ^[1]. The facts here say nothing about Taichi's licence or any paid plan, so check its own pages before you decide.

PyOverdrive is open source under the MIT License ^[3]. No price is stated for it.

Setting it up and daily use

CuPy provides wheels, which are precompiled binary packages, for Linux and Windows ^[1]. The facts here say nothing more about its setup or daily use, so check its own pages for those steps.

Taichi can be installed with pip, using pip install taichi -U ^[2]. The facts here describe no further steps for its daily use.

PyOverdrive is installed with pip, the Python package installer, straight from the GitHub repository ^[3]. It needs Python 3.12 or newer ^[3]. Once installed, python -m pyoverdrive --demo times the headline operations with stock NumPy and with PyOverdrive on your machine, in about 20 seconds ^[3]. The package is marked OS independent ^[3]. Its test suite has been run on Windows and on Linux x86-64 ^[3]. Its version is 1.0.0 and its status is Beta ^[3].

Things to check before you choose

Check whether your code already uses NumPy. PyOverdrive works on the supported NumPy calls in your existing code ^[3], and calls it does not support still run on stock NumPy ^[3]. CuPy's page says that in most cases it can be used in place of NumPy ^[1].

Check whether you have a graphics processor to use. CuPy's page describes GPU computing ^[1], and Taichi's compiler produces code for a GPU or a CPU ^[2].

Check your operating system. CuPy's sources list wheels for Linux and Windows ^[1], and PyOverdrive's test suite has been run on Windows and Linux x86-64 ^[3]. The facts here say nothing about macOS for any of the three, so check each project's own pages.

Check your Python version. PyOverdrive needs Python 3.12 or newer ^[3]. For the Python version CuPy and Taichi require, check the versions their own pages list.

Check how well a faster routine matches your results. PyOverdrive's self-check compares its routines with stock NumPy on your machine ^[3], and calls it does not support run on stock NumPy ^[3].

Check the maturity of each project. PyOverdrive's status is Beta ^[3], and the facts here give no status for CuPy or Taichi.

How to choose

If you want NumPy calls you already make to run on faster routines without rewriting them, PyOverdrive's claims describe that ^[3].

If you want to run NumPy and SciPy style array code on a graphics processor, CuPy's sources describe that ^[1].

If you want to write Python functions that compile to GPU or CPU machine code for parallel runs, Taichi's sources describe that ^[2].

If you want to check faster routines against stock NumPy on your own machine, PyOverdrive's claims describe the self-check ^[3].

If you want to see which routine a call would take before running it, PyOverdrive's claims describe pyoverdrive.explain() ^[3].

Frequently asked questions

Is CuPy a drop-in replacement for NumPy?

CuPy's page says that in most cases it can be used as a drop-in replacement for NumPy, which means existing NumPy code can often run on CuPy with few or no changes ^[1]. Check that the functions your code uses are covered on CuPy's own pages before you switch, because the facts available here do not list them. The facts for this page describe CuPy only as an open-source library for GPU computing with Python ^[1], so nothing more about compatibility is stated here.

Does Taichi publish a price?

The facts available for this page do not state a price, a licence or a paid plan for Taichi, so none is published here. Its page says Taichi can be installed with pip, using pip install taichi -U ^[2]. It also says its community has over 200 contributors from all over the world ^[2]. Check Taichi's own pages for cost and licensing before you decide.

Is PyOverdrive free to use?

PyOverdrive is open source under the MIT License ^[3]. No price is stated for it, and its status is Beta ^[3]. The package needs Python 3.12 or newer ^[3]. Read the MIT License text itself for the terms that apply to how you use the code, since this page does not restate them.

How do you install PyOverdrive with pip?

You install it with pip, the Python package installer, straight from the GitHub repository ^[3]. It needs Python 3.12 or newer ^[3]. Once it is installed, python -m pyoverdrive --demo times the headline operations with stock NumPy and with PyOverdrive on your machine, in about 20 seconds ^[3].

Can PyOverdrive be switched off for a single NumPy function?

You can turn off one faster routine with disable_path() or the PYOVERDRIVE_DISABLE setting ^[3]. Calling pyoverdrive.disable() restores the original NumPy functions, so calls go back to stock NumPy ^[3]. Calling pyoverdrive.explain() shows which routine a call would take and why, without running the call ^[3].

It is open source under the MIT License. ^[3]

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