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Rumor Ends. Nvidia Vera Rubin Mass Production Finalized, July Delivery to North American Tech Giants

TradingKeyMay 11, 2026 9:23 AM

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Nvidia's Vera Rubin AI platform is accelerating mass production, with trial production in June and initial shipments in July to major cloud providers like Microsoft and Google. Overcoming prior design rumors, Nvidia has finalized production plans with partners including TSMC (3nm chips), Foxconn, and Quanta. SK Hynix and Micron are supplying specialized memory and storage solutions. The platform, featuring Vera CPU, Rubin GPU, and NVLink6, offers significantly enhanced training and inference performance over Blackwell, with Nvidia projecting a tenfold increase in computing power output over the next decade and a potential trillion-dollar market impact.

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TradingKey - Nvidia ( NVDA )'s next-generation flagship AI platform, Vera Rubin, is accelerating its mass production pace. The timeframe for initial shipments has been officially finalized, debunking previous rumors surrounding the platform's design.

According to reports, Nvidia has finalized mass production plans with its ODM partners. Trial production is scheduled to begin in June, with initial deliveries to core North American cloud service providers starting in July. Microsoft ( MSFT ), Google ( GOOGL ), Amazon ( AMZN ), Meta ( META ), and Oracle ( ORCL) are all on the list of initial customers.

Previously, rumors circulated in the market regarding design adjustments or technical issues with Vera Rubin, similar to the turmoil before the release of the Blackwell GPU servers. However, leveraging extensive experience accumulated with supply chain partners in delivering next-generation AI hardware, Nvidia quickly finalized the mass production version. Relevant technical issues have been resolved, once again demonstrating its technical mastery in the high-end AI hardware sector.

The rapid progress of the Vera Rubin platform is inseparable from the deep adaptation and collaborative support of partners across the entire industry chain.

As the core chip supplier for Vera Rubin, TSMC ( TSM) adopted the 3nm process earlier this year to begin mass production of the chips. Meanwhile, contract manufacturing partners such as Foxconn, Quanta, and Wistron will roll out full-scale production of systems and racks starting in the second half of the year, with large-scale shipments expected as early as the third quarter of 2026.

SK Hynix's 192GB SOCAMM2 memory, customized specifically for this platform, has entered mass production. This memory module, based on the LPDDR architecture, offers more than double the bandwidth of traditional RDIMM memory and over 75% optimization in power efficiency, effectively breaking memory bottlenecks during the training and inference of large language models with hundreds of billions of parameters.

Micron ( MU) has simultaneously launched storage solutions for Vera Rubin. Its HBM4 memory features pin speeds exceeding 11Gb/s, with bandwidth approximately 2.3 times higher than the previous generation. The 192GB SOCAMM2 memory modules can provide up to 2TB of memory capacity per CPU, comprehensively supporting Vera Rubin's computing demands.

From a performance perspective, the Vera Rubin platform is a force to be reckoned with. Composed of several new chips including the Vera CPU, Rubin GPU, and NVLink6 switches working in synergy, its training performance is 3.5 times that of the previous-generation Blackwell platform. Running software performance has improved fivefold, the cost per token for inference can be reduced by 10 times, and the number of GPUs required to train MoE models is only a quarter of what was previously needed.

Nvidia has publicly stated that, relying on the hardware-software synergy of Vera Rubin, it expects to increase computing power output to 40 million times current levels within the next decade. The industry also generally anticipates that this platform will bring a new round of leapfrog advancements in AI computing power, with a potential market scale reaching trillions of dollars.

This content was translated using AI and reviewed for clarity. It is for informational purposes only.

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