This layer contains the core software infrastructure that powers training, serving, and productionizing modern AI systems. It includes model servers, distributed training frameworks, experiment management, ML observability, and orchestration platforms that abstract away GPU and cluster complexity. These startups define the backbone of how AI workloads are built, deployed, scaled, monitored, and iterated.
Compute & Performance Engineering focuses on improving model efficiency, throughput, latency, and hardware utilization. It includes model compression, kernel optimization, compiler acceleration, GPU scheduling, and performance profiling: everything that squeezes more performance per dollar out of GPUs. These startups are critical as model sizes grow and inference/training costs dominate P&Ls.
This category encompasses chips, networking, storage, cooling, and edge devices that physically power AI workloads. These startups focus on new compute architectures (AI accelerators), high-bandwidth interconnects for distributed training, advanced memory fabrics, and modern data center designs that support extreme power/thermal loads. As compute demand explodes, this layer is where fundamental performance breakthroughs happen.