Bring your model. We'll handle the silicon.

"The hardest part of this problem isn't writing fast kernels. It's making sure every single output is correct. We validate every kernel against PyTorch reference outputs on real silicon before anything ships. When a customer deploys a Kronos binary to a safety-critical device or Gaia hands back an optimized model for production inference, the performance they measured is the performance they get. That's the bar. We don't ship until it clears."

"Most optimization tools encode what humans already know into rules. We took the opposite approach. Our agents explore strategies that no engineer would try manually because the search space is too large and the iteration cost is too high. They generate kernel candidates, profile them on physical hardware, throw out what doesn't work, and keep what does. The system doesn't just apply known optimizations. It discovers new ones, and every discovery makes the next compilation faster."

“Every kernel we validate goes into KernelDB. Every future compilation starts from a richer starting point. Our first customer compilations were the most expensive. The cost curve points down with every customer we add. That's not a pricing strategy, it's a structural advantage that gets wider the longer we run."

"Teams were told portability meant giving up performance. We proved that's false. The same PyTorch model, zero code changes, ran 67% cheaper on alternative hardware than on NVIDIA — and faster in absolute terms. yasp optimizes, deploys, and exits. No runtime, no vendor SDK, no lock-in. Nothing left behind."

"Most teams come to us with the same story: the model works, but getting it onto the right hardware takes longer than training it did. Kronos gives them a validated binary for edge. Gaia finds optimizations their team didn't have time to look for in cloud. One pipeline, any chip, and they stop choosing hardware around their tooling and start choosing it around the problem."

"The search space for optimal AI execution is too large for any human or any static compiler to navigate. So we don't try to. Our agents write hardware-specific kernels, run every candidate on real silicon, and iterate until the output is verified correct. On one enterprise LLM they caught an algebraic equivalence no static tool would ever find and cut a single layer from 22.6ms to 0.65ms — a 6.25x speedup, every output identical."

"We spent years building AI infrastructure and kept hitting the same wall: a model that works everywhere in theory but only runs where the vendor lets it. yasp is our answer to that. You choose the hardware that's right for the job, our agents handle the optimization and deployment automatically, and the model runs because nothing stands in the way. You choose the hardware. yasp handles the rest."