Show HN: I built a tensor library from scratch in C++/CUDA
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...
Would be nice to see how inference speed stacks up against say llama.cpp
I'm also curious about how this compares to something like Jax.
Also curious about how this compares to zml.