Nvidia's Two-Front Battle: Record Revenue Meets a Fracturing Market
27.06.2026 - 06:35:29 | boerse-global.de
At first glance, Nvidia's numbers look like a portrait of unassailable dominance. The company posted $81.61 billion in quarterly revenue for the first quarter of fiscal 2027 — an 85% surge from a year earlier. Yet the stock closed Friday at €168.80, down roughly 7% over the past week and about 17% below its 52-week high. The disconnect tells a more complicated story, one involving a lost market in China, plunging GPU rental rates, and a shift in how artificial intelligence is actually being deployed.
The biggest single blow comes from Beijing. US export controls have effectively handed the Chinese AI-chip market to local rival Huawei, whose Ascend processors now command about 55% of that territory. Nvidia's share has shriveled to single digits. The result: revenue from Chinese data centers — once a $4.6 billion annual contributor — fell to practically zero in the most recent quarter. CEO Jensen Huang acknowledged the shift bluntly at the company's annual meeting on June 24, saying Nvidia has "largely abandoned" the Chinese market to its domestic competitor. Huang's recent trip to China alongside President Trump underscores the delicate balancing act the company must perform as geopolitical tensions persist.
Compounding the geographic headwind is a rapid cooling in the GPU rental market. Prices for Nvidia's B200 chips have tumbled 31% in just three weeks, from $6.11 to $4.22 per hour. The frantic scramble for hardware that defined the early AI gold rush is giving way to a more price-sensitive environment, as the industry pivots from training massive language models to running them in production. So-called inference workloads now account for two-thirds of global AI computing power in 2026, a shift that broadens the market but also commoditizes some of the computing demand. Adding to the pressure, Chinese models such as Z.ai's GLM-5.2 now match the performance of top-tier US systems at a fraction of the cost, squeezing Nvidia's software ecosystem.
Should investors sell immediately? Or is it worth buying Nvidia?
Not all pricing signals are negative. Amazon Web Services is raising the cost of its GPU instances by about 20% effective July 1, citing persistent shortages in high-bandwidth memory and DRAM that keep supply tight for Nvidia's Blackwell, Hopper, and Ampere architectures. And the industrial world is lining up for Nvidia's latest hardware. Roche is installing more than 3,500 Blackwell GPUs at an estimated investment of $300 million to $500 million to accelerate drug discovery. Early results from the pharma giant's Genentech unit suggest the chips can shorten certain development cycles by up to 25%. In Sweden, a massive data center being built in Boden will house as many as 10,000 Nvidia chips for use by state and industrial clients. The company's order book has swelled to €119 billion, providing a sturdy buffer against market mood swings.
On the shareholder front, Nvidia is returning cash at a furious pace. The board has authorized an $80 billion share buyback program, and the quarterly dividend has been raised to $0.25 per share, paid on June 26. Huang has committed to returning more than 50% of free cash flow to investors. Meanwhile, the transition to the next-generation Vera-Rubin architecture is already in full production, signaling that Nvidia is not resting on its current product cycle.
Technically, the stock is testing support near its 100-day moving average of €168.66 — almost exactly where it closed on Friday. The relative strength index sits at 38.2, close to the oversold threshold, while the shares trade below the 50-day average of €181.13. Institutional investors still hold about 65% of the float, but insiders have sold nearly two million shares over the past 90 days. Analysts remain broadly bullish, with a consensus price target of roughly $304, though some see the stock fairly valued closer to €261 given the emerging risks.
The market appears to be pricing a transition from the era of hype into a period of industrial-scale deployment, where Nvidia's hardware must prove itself as the indispensable foundation of the next wave of artificial intelligence — not just in training the biggest models, but in running them profitably around the world.
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