This article analyzes the growth of AI, highlighting the interplay between algorithmic advancements, hardware improvements, and data availability. It discusses key breakthroughs such as reinforcement learning and transformer architectures, as well as the infrastructure needed to support large-scale AI training.

DeepSeek-V3, trained on 2,048 NVIDIA H800 GPUs, addresses hardware limitations in scaling large language models through hardware-aware model co-design. Innovations such as Multi-head Latent Attention, Mixture of Experts architectures, and FP8 mixed-precision training enhance memory efficiency and computational performance, while discussions on future hardware directions emphasize the importance of co-design in advancing AI systems.