High performance IDE for Jupyter Notebooks

Zasper is built from the ground up for massive concurrency: a minimal memory footprint, exceptional speed, and thousands of connections at once.

It speaks Jupyter's wire protocol, so it runs the notebooks you already have.

Download Zasper today! Try it now

brew install zasper-io/tap/zasper

python_plotting_libraries_showcase.ipynb

A Zasper notebook with a rendered chart output

5× less CPU than JupyterLab

40× less RAM than JupyterLab

8 MB to download — the whole app, one binary

The gap widens as the load does

Both servers were run against the same workload — 10 requests per second per kernel, on an Apple M4 — from 2 concurrent kernels up to 100. Zasper starts ahead and stays ahead, and the distance grows with concurrency rather than shrinking.

Average CPU use against concurrent kernels, 2 to 100. Jupyter Server rises from 2.7% to a peak of 15.5% at 64 kernels; Zasper rises from 0.8% to 12%. The widest gap is 5 times less, at 4 kernels.

Peak RAM use against concurrent kernels, 2 to 100. Jupyter Server holds near 90 MB to 32 kernels, then climbs to 1.9 GB at 100; Zasper stays under 50 MB throughout. The widest gap is 38 times less, at 100 kernels.

CPU is averaged across each run; RAM is the highest figure observed, which is what “up to 40×” reports. At light load the difference is closer to 3–4× on both — the advantage is a function of concurrency, not a constant. Every point is recomputed from the raw runs in zasper-benchmark, where the harness and the methodology are public.

And it is a real IDE

The numbers above are only half the question. Every screen below is a running session, not a mock-up.

Your environments, already found

Conda, uv, venv and every Jupyter kernel you have installed — detected on open and one click from a blank notebook or a shell. Nothing to register by hand.

Launcher

A real shell, in the same window

Not an emulation. A full terminal that restores its session on reopen, in a tab beside the notebook it belongs to.

Terminal 1

A terminal tab open beside two notebook tabs in Zasper, showing the message “Restored session” followed by a directory listing at the shell prompt.

Version control, not an extension

Branch, stage, commit and read a side-by-side diff in the window you are already working in. JupyterLab ships no source control at all; git there is a third-party extension you install and maintain yourself.

Source control

Underneath all three: Zasper speaks Jupyter's wire protocol, reads and writes .ipynb at its own nbformat version, and runs the kernels you already have — so the notebooks in the screenshots above are the notebooks you have now.

Every kernel you already run

Zasper launches kernels through Jupyter's own kernelspec mechanism, so a kernel that runs under Jupyter runs here — without being registered again.

Python ipykernel & Xeus

R IRkernel

Julia IJulia

Ruby IRuby

JavaScript Deno

Go GoNb

Any kernel kernelspec

Python covers both implementations: ipykernel and Xeus (xeus-python). Conda, uv and venv environments are detected on open — the launcher above is showing nine of them, two of which are Xeus kernels. Installing kernels walks through adding your own.

Open source, with a commercial license too

Zasper is dual-licensed. Use it under the GNU Affero General Public License, or under a commercial license when the AGPL's terms do not fit what you are building.

Open source

AGPL-3.0-only

Running Zasper, on your own machine or on a server, needs nothing more. If you distribute Zasper, or let people use a modified version over a network, the AGPL requires you to make the corresponding source, including your changes, available under the same license.

Read the license

Commercial