High speed graphics rendering research with tinygrad/tinyJIT
I saw a tweet that tinygrad is so good that you could make a graphics library that wraps tg. So I’ve been hacking on a gtinygrad, and honestly it convinced me it could be used for legit research. The JIT + tensor model ends up being a really nice way to express light transport all in simple python, so I reimplemented some new research papers from SIGGRAPH like REstir PG and SZ and it just works. instead of complicated cpp its just a 200 LOC of python.
High speed graphics rendering research with tinygrad/tinyJIT launched on January 22, 2026 with 31 votes, #630 of 14,585 launches that month and more than 95% of that year's launches.
In plain words
Gtinygrad is a graphics rendering library built on tinygrad that enables researchers to implement light transport algorithms in Python. It wraps tinygrad's JIT compiler and tensor operations to simplify complex graphics research, allowing recent SIGGRAPH papers like REstir PG to be coded in approximately 200 lines of Python instead of complicated C++. The tool targets graphics researchers and developers who want to experiment with rendering techniques using a more accessible, high-level approach.
written from the facts on this page · September 2026
From the sources
You like pytorch? You like micrograd? You love tinygrad! ❤️ - GitHub - quantbagel/gtinygrad: You like pytorch? You like micrograd? You love tinygrad! ❤️
python examples/raytrace_demo.py About You like pytorch? You like micrograd? You love tinygrad!from github.com
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