Show HN: I built a tensor library from scratch in C++/CUDA

Hi HN,

Over the past few months, I've been building `dsc`, a tensor library from scratch in C++/CUDA. My main focus has been on getting the basics right, prioritizing a clean API, simplicity, and clear observability for running small LLMs locally.

The key features are: - C++ core with CUDA support written from scratch. - A familiar, PyTorch-like Python API. - Runs real models: it's complete enough to load a model like Qwen from HuggingFace and run inference on both CUDA and CPU with a single line change[1]. - Simple, built-in observability for both Python and C++.

Next on the roadmap is adding BF16 support and then I'll be working on visualization for GPU workloads.

The project is still early and I would be incredibly grateful for any feedback, code reviews, or questions from the HN community!

GitHub Repo: https://github.com/nirw4nna/dsc

[1]: https://github.com/nirw4nna/dsc/blob/main/examples/models/qw...

Show HN: I built a tensor library from scratch in C++/CUDA
github.com

Cool stuff! Is the goal of this project personal learning, inference performance, or something else?

Would be nice to see how inference speed stacks up against say llama.cpp

I noticed you interface with the native code via ctypes. I think cffi is generally preferred (eg, https://cffi.readthedocs.io/en/stable/overview.html#api-mode...). Although you'd have more flexibility if you build your own python extension module (eg using pybind), which will free you from a simple/strict ABI. Curious if this strict separation of C & Python was a deliberate design choice.

super n00b question , what kind of labtop do you need to do project like this? Is mac ok? or do you need dedicated linux labtop?

does gaming labtop works with windows? have always used mac for development because tool chain is so much easier, wondering if there is a difference between windows and linux for cuda development