NVIDIA continues to firmly occupy the core position in the global AI computing space by enter December. On the technology front, the company is pushing ahead with mass deployment of accelerators based on the Blackwell architecture. Its performance in large language model training, inference, and high-performance computing tasks in terms of efficiency and computing power still far outstrips its competitors.

At the same time, the Rubin architecture, which NVIDIA is expected to launch in 2025, has already drawn considerable market attention and is seen as an important breakthrough for the next stage of AI inference efficiency. In addition, the H200 GPU, as an enhanced version of the Hopper series, is seeing steadily rising demand in enterprise-level AI deployments thanks to its higher memory bandwidth and faster inference speed.

On the market side, the ongoing global expansion of AI infrastructure continues to drive strong growth in NVIDIA’s data center business. Demand from U.S. tech giants, AI model developers, and overseas government agencies remains robust.

Although some customers have started to explore developing their own accelerators, NVIDIA still enjoys extremely high customer stickiness thanks to its CUDA ecosystem, rich software toolchain, and complete AI development environment. It is expected that global AI server investment will continue to rise in 2024–2025, allowing NVIDIA to remain in a highly favorable cycle.

In terms of profitability, the company has significantly benefited over the past several quarters from its pricing power in high-end GPUs and from supply chain optimization. Gross margins remain at historically elevated levels, and the share of revenue from data centers continues to increase, making it the main engine of NVIDIA’s profit growth. Market consensus in December generally expects that, once Blackwell begins shipping at scale, NVIDIA will likely continue to deliver double-digit growth in the first half of next year.

Market Commentary:

Looking ahead, although competition is intensifying and regulatory variables remain, the expansion in AI model size, broader adoption of enterprise-level AI, and growing computing demand from autonomous driving and robotics will continue to support NVIDIA’s medium- to long-term development. Its technological leadership, ecosystem moats, and the global trend of accelerating compute build-out mean NVIDIA remains at the core of this AI “super-cycle.”