The most direct beneficiary of accelerated generative AI: What are the advantages of NVIDIA? 加速生成式 AI 的最直接受益者：英伟达优势究竟在哪？
Tianfeng Securities believes that Nvidia still maintains a significant competitive advantage in three aspects: the development cost of data training and inference, the cost of data centers, and the ability of ultra-large-scale parallel computing. The new generation of large models with rapidly increasing parameter size, combined with code-driven software and plugin ecosystems, may herald the beginning of a new round of growth.
NVIDIA delivered an impressive Q2 report last week, with both revenue and profits skyrocketing and far exceeding expectations.
Despite the market volatility after the earnings report, NVIDIA's stock price did not rise significantly. However, the stock has already risen over 200% this year, carrying the weight of market expectations.
As NVIDIA CEO Jensen Huang declared the start of the "Generative AI Competition," investors are focusing on NVIDIA's AI chip supply and long-term development.
According to the analysis team at TF Securities, NVIDIA's full-stack (software + hardware + network) may be the most direct beneficiary in accelerating generative AI innovation and widespread applications!
Firstly, generative AI is still in its early stages, and the demand for accelerated computing is expected to increase exponentially. The data center market will rapidly develop towards AI acceleration, with recommended search in consumer internet, large-scale model inference training, and vector databases constituting the most important demands for accelerated computing, thereby driving the rapid growth of GPU demand.
Looking at downstream applications, Google and Bing's search applications for large-scale models are just the first step. Consumer internet content and design generation, enterprise intelligent software and data analysis, and personal intelligent assistants will all require new AI acceleration capabilities.
TF Securities believes that NVIDIA's competitive advantages remain significant in the following areas:
Development costs for data training and inference;
Data center costs;
Massive parallel computing capabilities.
TF Securities also stated that even though the supply-demand situation is still tight at present, there may be fluctuations in GPU demand in the short term. The key to whether cloud computing companies, startups, software companies, and traditional enterprises will increase their purchases further lies in whether existing generative AI capabilities can achieve sufficient commercial prospects and momentum.
At the same time, the new generation of large-scale models with rapidly increasing parameter quantities, combined with code-driven software and plugin ecosystems, may herald the beginning of a new wave of growth.