We make GPU compute go further.

Kernova started with a frustration every AI infrastructure engineer knows: a training run that should take days takes weeks, and the profiler shows the GPU idle half the time — waiting on memory, on kernels that were compiled for a generic shape, on operators nobody had time to tune.

The usual answer is to hire a kernel specialist and wait. Hand-tuned CUDA or Triton kernels can unlock dramatic speedups, but the expertise is scarce, the work is slow, and every model change starts the cycle over. Most teams simply accept the waste and pay for more hardware instead.

We asked a different question: what if the search for a faster kernel could be automated — and, just as importantly, verified? A faster kernel is only useful if it produces the same results. So Kernova pairs AI-driven kernel generation with rigorous numerical validation, so every optimization ships with evidence, not hope.

Today we work with early-access teams running training and inference at scale, and we hold ourselves to a simple standard: every number we report comes with its methodology, its hardware, and its baseline. That is what "more intelligence, less compute" means to us.