AI s Memory Problem скачать в хорошем качестве

AI s Memory Problem

Modern AI infrastructure faces a significant hurdle because input text expands into massive data structures called KV caches during processing. For frontier models, a single million-token context can require terabytes of high-bandwidth memory, making long-context tasks an immense hardware challenge. Nvidia’s upcoming Vera Rubin and Rubin Ultra racks address this by packing dozens of GPUs and vast memory into fridge-sized units. However, even these advanced systems can only support three or four concurrent users at extreme context lengths due to these memory constraints. Consequently, maintaining performance for million-token inputs requires a sophisticated hierarchy of HBM, RAM, and SSDs to manage data flow. The transition from standard to "Ultra" hardware reflects a strategic shift toward prioritising memory capacity over raw compute speed to sustain next-generation AI workloads.