NVIDIA Corporation (NVDA.US), the dominant force in AI chips, is initiating mass production of its H200 AI training and inference accelerators for clients in China, according to CEO Jensen Huang. The H200 is based on the Hopper architecture launched in March 2022. This move signals positive progress in the U.S. chipmaker's efforts to re-enter the critical Chinese market for AI computing infrastructure. Should the H200, which faces a 25% additional cost due to U.S. government tariffs, achieve significant sales volume in China, it would represent a substantial incremental positive for NVIDIA's fundamental growth prospects. This is particularly relevant as NVIDIA's current stock price trajectory remains in a consolidation phase. Notably, neither the company's official quarterly guidance nor the "super AI blueprint" presented at Monday's GTC conference—projecting at least $1 trillion in revenue by 2027—currently incorporates revenue expectations from the Chinese market.

Following a major keynote at NVIDIA's GTC event where he unveiled the next-generation AI computing infrastructure, the Vera Rubin architecture, Huang stated at a press conference on Tuesday local time that NVIDIA has received U.S. government permission to sell the H200 AI chip to "many large customers in China" and is currently in the process of "restarting our mass production." He emphasized that the outlook has changed considerably compared to just weeks ago. "Our H200 supply chain is being restarted," Huang remarked during an event at the annual GTC conference in San Jose, California. The CEO had previously unveiled a series of new products and provided investors with updated financial guidance during the conference's opening speech.

NVIDIA has been striving to restore its AI chip sales in China in recent years. Due to longstanding U.S. government restrictions on chip exports, this once heavily relied-upon market had been largely closed to such AI computing infrastructure products. Although the Trump administration has recently begun allowing NVIDIA and its primary competitor, AMD (AMD.US), to sell performance-restricted versions of their AI chips to China, this still requires formal U.S. government approval and is subject to a 25% tariff. The U.S. government permits H200 exports to China under specific conditions, with the 25% fee/tariff acting as a concession—a policy compromise allowing exports while generating revenue. In contrast, more advanced AI chip products, such as those based on the Blackwell architecture or AMD's Instinct MI450 series, are still considered more sensitive technologies under U.S. policy and are not currently eligible for export licenses, meaning they are not subject to such tariff policies. It is important to note that this semiconductor tariff policy for NVIDIA and AMD excludes chips destined for U.S. data centers, consumer devices, and industrial use; tariffs do not apply to H200/MI325X chips used directly within the United States.

NVIDIA has not yet factored any revenue prospects from the Chinese data center market into its financial forecasts. The Data Center segment, currently NVIDIA's core business, provides immensely powerful AI computing infrastructure for global data centers through its H100/H200 and Blackwell/Blackwell Ultra architecture AI GPUs. During an earnings call last month, the company indicated it had only received a preliminary permit from the U.S. government to ship small quantities of the H200 AI chip to China at that time. Although the H200's overall performance is significantly lower than NVIDIA's current Blackwell/Blackwell Ultra architecture AI chips used for training and running large AI models, it remains popular in the sanctioned Chinese market due to its strong AI inference capabilities, the pervasive CUDA ecosystem among global AI developers, and ease of deployment. China previously accounted for approximately a quarter of NVIDIA's total revenue but now constitutes only a small fraction. Despite robust global demand for NVIDIA's AI chips, China remains the world's largest single semiconductor market, making it crucial for NVIDIA's long-term fundamental prosperity.

NVIDIA received verbal approval from former U.S. President Donald Trump in December of last year to sell the H200 to some Chinese customers, but the chipmaker has not yet recognized any H200 revenue from China based on these permissions. Regulators in Washington have also established additional manufacturing and tariff rules, creating further hurdles that have slowed the formal approval process and made a full return to unrestricted sales unlikely. With Huang's latest comments indicating that H200 AI chip mass production is being restarted, NVIDIA may soon begin recognizing revenue from Chinese market H200 sales. Previous media reports suggested that H200 AI chips shipped to China are subject to additional U.S. routine inspections and the full 25% tariff. U.S. government officials are also reportedly considering limiting purchases for each Chinese customer to 75,000 H200 AI chips, with a total export cap of up to 1 million processors. Demand for the H200 AI chip in China is likely very strong; the primary constraint on deals is not demand but U.S. government policy and approval processes. Recent media reports indicated that actual order demand from Chinese tech companies for H200 AI chips since 2026 has exceeded 2 million units, while NVIDIA's inventory at the time was only about 700,000 H200 chips.

**China Market – A Significant Incremental Positive for NVIDIA** NVIDIA's stock price closed down 0.7% at $181.93 during Tuesday's trading session, bringing its year-to-date decline to 2.5%, underperforming the S&P 500 index. From a fundamental perspective, if H200 AI chips can flow into the Chinese market on a relatively large scale, it would be a material incremental positive for NVIDIA. This is especially significant given that China once contributed about a quarter of NVIDIA's revenue, a share that has now dwindled to a minimal level. Furthermore, NVIDIA's strong quarterly guidance provided in February did not include any revenue prospects from Chinese data centers, and the company's recent outlook for this segment remains zero. This implies that once H200 shipments begin to normalize, even if not fully unrestricted, it would create room for upward revisions to NVIDIA's current valuation models and market growth expectations.

In terms of underlying comprehensive performance, the H200 is clearly one or even two generations behind the current Blackwell architecture and the Vera Rubin architecture, which Huang just announced will enter mass production by the end of the year. The H200 belongs to the classic Hopper architecture, with single-card specifications of 141GB HBM3e memory, 4.8TB/s bandwidth, and approximately 4 PFLOPS FP8 performance. NVIDIA has publicly demonstrated that the GB200 NVL72 can offer up to 15 times the performance/revenue opportunity advantage over the Hopper H200 in certain inference scenarios. Furthermore, the official specification for Vera Rubin is a 10x improvement in performance-per-watt and a 10x reduction in token cost compared to Blackwell. However, these disparities do not seem to hinder the H200's suitability for the current realities of the U.S.-sanctioned Chinese market. The H200 offers nearly a 6x performance improvement over the H20, a product NVIDIA previously designed specifically for China. Amid the global AI inference wave, enterprises genuinely need mature chips that can be deployed immediately, run large model inference, and offer larger memory and higher bandwidth. While AI training, a domain where NVIDIA's AI GPUs are nearly monopolistic, requires more powerful AI compute cluster versatility and rapid iteration of the entire computing system, the AI inference side, after the规模化落地 (large-scale implementation) of cutting-edge AI technologies, places greater emphasis on cost-per-token, latency, and energy efficiency. "The era of AI inference has arrived," Huang stated at the GTC conference on Monday. "And the demand for inference is still rising," he added. Therefore, the H200's 141GB of HBM3e memory remains very attractive for long-context, larger-batch, retrieval-augmented generation (RAG), and enterprise-scale, high-efficiency batch deployment of AI inference clusters. Coupled with the strong demand driven by the CUDA ecosystem, it represents "high-end usable computing power under constraints" for the Chinese market. Simultaneously, CUDA, CUDA-X, ready-made model adaptation, development toolchains, and operational experience significantly reduce migration and implementation costs for Chinese clients.

For Wall Street institutional capital, this is not a grand narrative of "NVIDIA relying on China for a turnaround." Instead, it represents an additional, potentially severely underestimated, upside in Chinese market demand layered on top of an already strong global AI computing infrastructure thesis. During the GTC conference keynote in the early hours of March 17th Beijing time, CEO Jensen Huang presented NVIDIA's "unprecedented super blueprint for AI computing revenue" in the AI infrastructure domain. He informed global investors that, driven by strong demand for Blackwell architecture GPU computing power and the even more explosive demand expected from the upcoming Vera Rubin architecture AI computing system, the company's future revenue scale in the AI chip field could reach at least $1 trillion by 2027. This figure far exceeds the $500 billion AI computing infrastructure blueprint presented at the previous GTC conference targeting 2026. As model scale, inference chains, and multimodal/agentic AI workloads push computing power consumption to expand exponentially, the capital expenditure focus of tech giants is increasingly concentrating on AI computing infrastructure. Global investors continue to anchor the "AI bull market narrative"—centered on expectations for new product iterations and AI compute cluster deliveries from NVIDIA, Google's TPU clusters, and AMD—as one of the most certain growth investment themes in global equity markets. This also implies that investment themes closely related to AI training/inference, such as power, liquid cooling systems, and optical interconnect supply chains, will continue to rank among the stock market's hottest investment sectors, following leaders like NVIDIA, AMD, as well as Broadcom, TSMC, and Micron, even amid geopolitical uncertainties in the Middle East.

According to Wall Street giants Morgan Stanley, Citi, Loop Capital, and Wedbush, the global wave of investment in artificial intelligence infrastructure, centered on AI computing hardware, is far from over and is merely in its beginning stages. Propelled by an unprecedented "storm of demand for inference-side computing power," this round of global AI infrastructure investment, extending through 2030, is projected to reach a scale of $3 trillion to $4 trillion.