Kernel‑level innovation and hardware‑aware modeling

Our team is driving fundamental innovation at the kernel level to push the boundaries of efficiency in large-scale AI workloads. We are rethinking core attention mechanisms and computational pathways to deliver breakthroughs in performance, memory optimization, and scalability.

By redesigning execution flows, introducing advanced quantization strategies, and leveraging emerging hardware capabilities, we aim to eliminate bottlenecks in both compute and communication layers. This project focuses on achieving end-to-end acceleration without compromising accuracy or reliability, enabling models to handle longer contexts and higher throughput at significantly lower cost. Through tight algorithm–hardware co-design and deep integration with production systems, we are building the foundation for next-generation AI infrastructure that is faster, leaner, and more sustainable.

Together, these advancements deliver measurable gains in tokens per second, cost per generated token, and energy efficiency while preserving output quality.