yasp Information

AI progress should not be bottlenecked by hardware. yasp is the intelligence layer between AI models and the silicon they run on. Bring a trained PyTorch model and a target accelerator; we produce optimized, validated, hardware-specific code. No rewrites, hand-written kernels or vendor lock-in. yasp automates optimization and deployment across accelerators, improving throughput and latency while validating every result against the reference model. Why it matters: every accelerator has its own SDK, compiler, memory hierarchy and runtime. Supporting multiple targets has traditionally meant separate engineering efforts for each one. yasp turns that complexity into one automated workflow. Validated on real hardware from AMD, NVIDIA and Amazon and counting. Teams in Munich, Montreal and the US