Nvidia CEO Jensen Huang has forcefully rejected analyst reports claiming the company's next-generation Vera Rubin AI platform faces production delays, stating the systems are already in production and will ship in massive volumes this year. The denial directly contradicts recent warnings from KeyBanc Capital Markets and SemiAnalysis, which flagged thermal issues, high-bandwidth memory qualification problems, and manufacturing difficulties with networking components as potential risks to the Vera Rubin rollout timeline.

What Is Vera Rubin and Why Does It Matter?

Vera Rubin represents one of the most critical product launches in Nvidia's history. The platform is designed to dramatically reduce artificial intelligence inference costs, which is the computational work required to run trained AI models on real data. Unlike training, which happens once, inference happens millions of times as users interact with AI systems. Vera Rubin's ability to handle larger model sizes, generate tokens faster, and scale GPU clusters more efficiently could cement Nvidia's dominance in the AI inference market for years to come.

The financial stakes are enormous. Nvidia has projected a staggering $1 trillion in combined revenue from sales of Vera Rubin and its predecessor Blackwell processors across 2026 and 2027, double the $500 billion revenue forecast it previously issued for the two architectures in the 2025 to 2026 timeframe. Wall Street consensus estimates point to earnings-per-share growth accelerating to 88 percent in fiscal 2027, up from 60 percent the prior year.

"Vera Rubin is already in production. Giant amounts of production incoming," Huang stated, calling the delay reports "not true."

Jensen Huang, CEO at Nvidia

How Is Nvidia Securing the Memory Supply Chain for Next-Generation AI?

Behind the scenes, Huang is locking in the critical memory supply chain required to power Vera Rubin and future platforms. Industry analysts estimate that SK Hynix has secured between 50 percent and 70 percent of Nvidia's anticipated orders for HBM4, the next generation of high-bandwidth memory essential for scaling AI workloads. In early June, Nvidia and SK Hynix formalized a multiyear partnership focused on co-developing advanced memory solutions for what Huang calls "AI factories."

HBM4 represents a critical step forward in memory technology because AI applications are fundamentally memory-bound. Training and running large language models requires transferring enormous volumes of data between hundreds of thousands of graphics processing units (GPUs) at extremely low latency. Conventional DRAM struggles to keep pace, leading to underutilized compute and longer training times. HBM solves this by stacking DRAM dies and connecting them through vertical interconnects, delivering substantially higher bandwidth while consuming less power.

• SK Hynix's Market Share: The South Korean memory manufacturer has secured between 50 percent and 70 percent of Nvidia's HBM4 orders, giving it a disproportionate share of the advanced memory market.
• Huang's Urgency on Supply: Reports dating back to 2024 indicate Huang pressed SK Hynix to accelerate its HBM4 manufacturing roadmap by six months, reflecting concerns about insufficient capacity.
• Competitive Landscape: Three companies possess the technology and manufacturing expertise to produce HBM4 at scale: SK Hynix, Samsung, and Micron Technology, each aggressively expanding capacity.

What Is Nvidia's Strategy Beyond AI Chips?

While the Vera Rubin rollout and HBM4 supply chain developments command immediate attention, Nvidia is simultaneously planting flags in the emerging physical AI and robotics market. The company recently released Cosmos 3 Edge, a generative world foundation model designed specifically for robotics applications. Unlike large language models that process text, Cosmos 3 Edge is built to predict next-frame visual sequences with impressive speed and accuracy, enabling robots to navigate and interact with physical environments more effectively.

Three characteristics make Cosmos 3 Edge particularly notable. First, its next-frame prediction capability is substantially faster than existing alternatives, a critical requirement for real-time robotic control. Second, the model demonstrates rapid adaptation to new environments, reducing the training burden for robotics developers. Third, and perhaps most strategically significant, the model is open-source, a move that echoes Nvidia's earlier decision to seed its CUDA software platform into universities and research labs, which ultimately made CUDA the default programming environment for AI development and created a durable competitive moat.

"AI factories are the engines of the next industrial revolution, and advanced memory is essential to their performance. SK Hynix has been an extraordinary partner to Nvidia, playing a central role in delivering advanced memory technologies for Nvidia AI computing platforms," Huang stated regarding the partnership.

Jensen Huang, CEO at Nvidia

What Do Wall Street Analysts Expect From Nvidia's Stock?

The 12-month median price target for Nvidia stock sits at $300, implying roughly 45 percent upside from current levels, with 62 of 66 covering analysts rating the stock a buy. Some projections are considerably more bullish. If Nvidia trades at 25.5 times earnings, in line with the Nasdaq-100 index, and hits estimated earnings per share of $16.06 by the end of fiscal 2029, the stock price could reach $409, roughly double recent trading levels.

The optimism reflects confidence that Vera Rubin will deliver on its promise to accelerate Nvidia's earnings growth and maintain the company's commanding position in AI infrastructure. However, the analyst warnings about production delays, though dismissed by Huang, underscore the execution risks inherent in scaling manufacturing for next-generation semiconductor platforms at the volumes Nvidia is projecting.