[AI Infrastructure] The Era of 50,000 GPUs: Success Conditions for AI Infrastructure Businesses

Neoclouds are next-generation AI platforms that provide environments optimized for AI workloads. Unlike the general-purpose services of existing Hyperscalers, they respond agilely to customers' special needs through a 'Boutique' approach equipped with GPU-centric large-scale parallel computation and high-bandwidth networking.

Their core competitiveness is Full Stack optimization capability from a Hardware–Software Co-design perspective. Beyond simply providing hardware, they maximize efficiency by eliminating bottlenecks between GPU computation, network, and storage.

For GPU computation optimization, CUDA Graphs and Kernel fusion reduce kernel execution latency, while Mixed precision reduces computation volume and memory usage. Additionally, Micro-batch scheduling is used to minimize the gaps (Pipeline Bubble) that occur during pipeline parallel processing.

On the network side, RDMA and InfiniBand topology optimization, along with reduced data transfer latency through GPUDirect RDMA, are important. In particular, NVIDIA RAIL technology and NCCL library optimization manage the bottlenecks and delays that occur when tens of thousands of GPUs communicate.

In the storage domain, the key is optimizing data preprocessing speed and disk I/O performance to solve the DataLoader starvation problem, where GPUs fall idle while waiting for data.