Fwiw, they did say they got up to 20x improvement but given the issues we both mention this may not be surprising given that this seems to be an outlier by their own omission.
every time we generate a kernel, we profile it on real GPUs (serverless) so you see how it runs on specific architectures. not just "trust the code" we show you what it does. still early, but it’s helping people move faster
I get that you profile. I liked that part. But even as the other commenter says, it's unclear how to evaluate given the examples. Showing some real examples would be critical to sell people on this. Idk, maybe people blindly buy too but personally I'd be worried about integrating significant tech debt. It's easy to do that with kernels or anytime you're close to the metal. The nuances dominate these domains
I'd suggest you use that NVIDIA connection and reach out to the HPC teams there. Anyone working on CUTLASS, TensorRT, cuTensor, or maybe event the CuPy team could give you a lot better advice than me.
The project seems very naive. CUDA programming sucks because there's a lot of little gotchas and nuances that dramatically change performance. These optimizations can also significantly change between GPU architectures: you'll get different performances out of Volta, Ampere, or Blackwell. Parallel programming is hard in the first place, and it gets harder on GPUs because of all these little intricacies. People that have been doing CUDA programming for years are still learning new techniques. It takes a very different type of programming skill. Like actually understanding that Knuth's "premature optimization is the root of evil" means "get a profiler" not "don't optimize". All this is what makes writing good kernels take so long. That's even after Nvidia engineers are spending tons of time trying to simplify it.
So I'm not surprised people are getting 2x or 4x out of the box. I'd expect that much if a person grabbed a profiler. I'd honestly expect more if they spent a week or two with the documentation and serious effort. But nothing in the landing page is convincing me the LLM can actually significantly help. Maybe I'm wrong! But it is unclear if the lead dev has significant CUDA experience. And I don't want something that optimizes a kernel for an A100, I want kernelS that are optimized for multiple architectures. That's the hard part and all those little nuances are exactly what LLM coding tends to be really bad at.