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

Thanks! To be honest, it started purely as a learning project. I was really inspired when llama.cpp first came out and tried to build something similar in pure C++ (https://github.com/nirw4nna/YAMI), mostly for fun and to practice low-level coding. The idea for DSC came when I realized how hard it was to port new models to that C++ engine, especially since I don't have a deep ML background. I wanted something that felt more like PyTorch, where I could experiment with new architectures easily. As for llama.cpp, it's definitely faster! They have hand-optimizing kernels for a whole bunch of architectures, models and data types. DSC is more of a general-purpose toolkit. I'm excited to work on performance later on, but for now, I'm focused on getting the API and core features right.

Both uses cublas under the hood. So I think it is similar for prefilling (of course, this framework is too early and don't have FP16 / BF16 support for GEMM it seems). Hand-roll gemv is faster for token generation hence llama.cpp is better.