What happens when you use AI to optimize AI and make AI models run fast anywhere?

Tired of choosing between performance and freedom? We sit down with Stefan Crossin, CEO and co‑founder of YASP, to unpack how a hardware‑aware AI compiler can speed up training, simplify deployment, and finally make model portability real. The story starts with a distributed team in Freiburg and Montreal and moves straight into the heart of the problem: most AI groups burn time on infrastructure and juggle separate stacks for training and inference, all while staying tethered to one dominant vendor’s software ecosystem.

Stefan lays out a different path. YASP converts models into a clean intermediate representation, plugs into the tools teams already use, and applies a closed‑loop optimization system that learns the target hardware. Instead of forcing a new language or workflow, a few lines of integration unlock dynamic kernel generation, graph‑level tuning, and one‑click deployment to different chips, clouds, or edge devices. The result is a practical bridge between “write once” ideals and real‑world performance, where being hardware‑aware—not hardware‑bound—delivers speed without lock‑in.

We also dive into the market dynamics behind portability. Incumbents protect moats; challengers need bridges. Cloud providers fear shorter runtimes but win when customers get more value per dollar and per watt. With credible benchmarks showing meaningful gains in training and inference, YASP is courting chip makers, CSPs, and end users through a focused beta, a clear roadmap to launch, and a business model that combines free access with subscription tiers. If you’ve been waiting for proof that AI can be both faster and freer across architectures, this conversation makes the case with clarity and detail.

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