NVIDIA has achieved new records in all eight MLPerf Training v3.0 AI tests with its H100 accelerators, whether used individually or as part of clusters. For instance, a commercial cluster developed by Injection AI and CoreWeave cloud platform, consisting of 3,584 H100 accelerators, successfully trained the GPT-3 AI model in under 11 minutes.



NVIDIA demonstrated the exceptional performance of its H100 accelerators across various MLPerf tests, including large language Models (LLM), recommendation systems, computer vision, medical image processing, and speech recognition. These accelerators were the only ones to pass all eight tests, showcasing the versatility of NVIDIA's AI platform. Furthermore, optimization of the entire NVIDIA stack resulted in an almost linear increase in performance for LLM tests when scaling from hundreds to thousands of accelerators. The energy efficiency of the H100 accelerators was also highlighted.

The updated MLPerf benchmark for recommendation systems, which includes a more extensive data set and a newer model, better reflects the challenges faced by cloud providers. NVIDIA was the sole company to present results for this improved test. Additionally, the company showcased MLPerf results for the L4 and Jetson platforms. NVIDIA Grace Hopper is expected to participate in the next round of MLPerf.

In this round of tests, ten companies showcased their results using the NVIDIA platform. Major system manufacturers like ASUS, Dell Technologies, GIGABYTE, Lenovo, and QCT were among the applicants. Over 30 measurements were conducted on H100 accelerators. NVIDIA emphasized the transparency and objectivity of these tests, providing users with reliable MLPerf results for informed system purchasing decisions.

This news sharply contrasts with reports from the WSJ stating that the US administration intends to ban the supply of NVIDIA accelerators without a special license. Previous restrictions led NVIDIA to swiftly release stripped-down A800 and H800 models for the Chinese market to bypass the supply ban, but this loophole may soon be closed. Furthermore, the US administration plans to prohibit Chinese companies from renting cloud capacities from Western companies.

Adding personal thoughts: It is interesting to see NVIDIA pushing the boundaries of AI performance with its H100 accelerators and gaining recognition in the MLPerf tests. However, the potential ban on supply and restrictions on Chinese access to cloud capacities highlight the complex geopolitical challenges that can impact the AI industry.

I think it's critical to delve deeper into the US Administration's plan to restrict NVIDIA accelerators and its implications for the Chinese cloud market. These restrictions are primarily aimed at limiting China's access to advanced technologies that are essential for developing cutting-edge artificial intelligence and high-performance computing capabilities. The reasoning behind this is that such technologies may be leveraged for military advancements, creating significant national security concerns for the US.

NVIDIA's graphics processing units (GPUs) are among the most powerful tools for AI and machine learning. By restricting access to these accelerators, the US government is essentially trying to hinder any potential technological leap that China could make in these fields. This could lead to a substantial impact on Chinese tech giants who rely on these resources for their operations, stunting their growth compared to their US counterparts.

Moreover, the plan could result in a ripple effect throughout the global cloud ecosystem. With such restrictions in place, we may witness companies in China scrambling to find alternatives—possibly pivoting towards locally-developed technology or seeking partnerships with non-US tech firms. However, the challenge lies in the fact that many leading-edge technologies are primarily developed and produced in the US.

Also, for cloud computing providers worldwide, this situation could lead to increased demand for other types of computing resources. Companies might start investing in developing their own proprietary solutions, resulting in a push for more innovation within the US. There's potential for US firms to fill the gaps left by NVIDIA and create alternative solutions tailored to meet similar performance standards.
Navigating this new regulatory landscape is fraught with challenges. There could be a need for companies to ensure compliance not just with US regulations, but also with international laws, especially when dealing with cross-border data flows and computing resources. In summary, the US Administration's plan to restrict NVIDIA accelerators and Chinese cloud services could reshape the competitive landscape and push for rapid innovation but would also create a complex web of legal and operational challenges.

The US administration plans to restrict NVIDIA accelerators and Chinese cloud rentals to curb China's advancements in artificial intelligence and high-performance computing. By limiting access to cutting-edge technology, the US aims to protect national security and maintain a competitive edge in critical industries. These restrictions will impact Chinese tech companies' ability to procure advanced semiconductors and cloud services, potentially slowing their technological progress. This move reflects broader efforts to control the transfer of sensitive technologies and mitigate potential security threats.

NVIDIA's H100 accelerators are the 800-pound gorilla in the AI room, crushing all competition in the MLPerf Training v3.0 AI tests. But let's be real, this dominance comes at a cost. The potential ban on supply to certain countries and restrictions on cloud capacities are a stark reminder that the AI industry is a pawn in a much larger game of geopolitical chess. I'm tired of being held hostage by NVIDIA's proprietary technology and the constant threat of supply chain disruptions.