Show HN: I Wrote Inference for Qwen3 0.6B in C/CUDA

I'm excited to share with you my latest project, a C/CUDA implementation of inference for the popular Qwen3-0.6B model. This project has been a thrilling adventure into the world of deep learning, allowing me to explore the fascinating realm of neural networks and their applications.

To get started, I obtained the pre-trained `model.safetensors` file from the Hugging Face repository for Qwen3-0.6B. I then simply placed it in the root directory of my local repository. The next step was to build a shared library using this model, which would serve as the foundation for our Python-based chat application.

The implementation is built into a shared library that can be used by the `python chat.py` script for chatting. To achieve this, I utilized `run.c` as the entry point, which loads the model and prints the generated tokens. While this project may not seem particularly useful on its own, it serves as an excellent starting point for anyone looking to learn about C, CUDA, and deep learning libraries.

A key aspect of this project is that it only supports CUDA as a backend. As I wanted to gain hands-on experience with these technologies, I chose to focus on building something functional. In doing so, I successfully implemented the Qwen3-0.6B model in C/CUDA, and the architecture for the other Qwen3 models can be adapted with minimal modifications.

The Qwen3 models have a uniform architecture, which means that this implementation could theoretically be used with any of these variants (just remember to update the hardcoded layer and head counts in `json.c`). This project offers numerous potential extension points, making it an attractive choice for anyone interested in learning or contributing to the world of deep learning.

For those curious about diving into similar projects, I highly recommend exploring the possibilities that this code has to offer. Who knows what innovative applications will emerge from these initial steps?