Fix idle GPUs by optimizing data, scheduling, and prefetch pipelines

Fix idle GPUs by optimizing data, scheduling, and prefetch pipelines targets a $8.4B = 42,000 organizations running production GPU workloads x $200K ACV total addressable market with medium saturation and a year-over-year growth rate of 25% annual growth in MLOps and GPU spend adoption.

Key trends driving demand: Model scale-up -- larger models increase distributed IO and exacerbate data locality issues, creating demand for smarter scheduling and prefetching.; Cloud GPU commoditization -- more teams rent GPUs which puts pressure on utilization and cost efficiency, making optimization tools valuable.; Richer telemetry and APIs -- cloud and infra vendors expose metrics that make predictive placement and caching feasible..