Tesla is looking to enter the modular AI data center hardware market, as indicated by a newly submitted trademark application for a product named “Megapod.” This development follows the company's recent decision to discontinue its in-house AI training system, Dojo, less than a year ago.
The trademark application, which carries the serial number 99893717 and was filed with the U.S. Patent and Trademark Office, is labeled as an intent-to-use application. This suggests that Tesla is reserving the name for a product that has yet to be released. Notably, the filing includes very detailed descriptions of the intended system. It outlines “modular data center hardware systems for artificial intelligence computing,” which consists of computer servers, AI data processing hardware, networking gear, power distribution units, and cooling systems.
In essence, the “Megapod” is poised to be a comprehensive solution for AI data center infrastructure — not just standalone components like chips or batteries, but a complete package including servers, networking, and cooling systems essential for AI training and inference.
However, Tesla will be entering a highly competitive arena where Nvidia is the current market leader. The Nvidia GB200 NVL72 serves as the standard for modular AI computing. This system boasts liquid cooling and is designed to function as a massive GPU, featuring 72 Blackwell GPUs and 36 Grace CPUs. Nvidia's DGX SuperPOD can cluster these units together, scaling them to accommodate over 9,000 GPUs. Other major players, such as Dell and Supermicro, also leverage Nvidia technology, which further complicates Tesla's entry into the space.
Moreover, there is a challenge regarding the name itself. A company named Submer already offers a product called “MegaPod,” which is a prefabricated, immersion-cooled data center rated up to 800 kW. While Tesla’s trademark application is categorized differently, the name is not entirely unique.
Another significant hurdle for Tesla is its lack of a dedicated compute hardware business. Currently, Tesla’s AI training operations at Gigafactory Texas rely on approximately 67,000 Nvidia H100-equivalent GPUs, meaning it depends on Nvidia rather than competes with it in the hardware space. Furthermore, Tesla's attempts at developing its own AI hardware, such as the Dojo supercomputer, have faced challenges, leading to its cancellation. Although Elon Musk has hinted at a revival of the Dojo using iterations from Tesla's AI chips, progress has been slow.
Tesla does have a robust presence in the energy sector, particularly with offerings like the Megapack and Megablock energy storage systems. These products serve as crucial buffers for AI data centers, with Musk's xAI having invested around $1 billion in Megapacks to support its operations. Therefore, there is potential for the Megapod concept to incorporate aspects of Tesla’s existing energy solutions.
The timing of the Megapod initiative is noteworthy, as Tesla has not significantly benefited from the ongoing AI infrastructure boom unlike other tech companies that have seen market valuations rise due to AI advancements. While stock prices of firms like Nvidia have soared, Tesla's shares have dropped over 20% year-to-date amid waning EV tax incentives and tighter profit margins.
This context suggests that the Megapod may be Tesla's strategy to associate itself with the AI trend. Historical patterns reveal a series of AI-related announcements from Tesla, often without substantial hardware deployment. The reality is that Tesla excels in the battery sector while facing challenges in compute hardware. A Megapod that focuses on integrating power and cooling solutions for AI setups aligns with Tesla’s strengths, while marketing a server system to compete with Nvidia may be overly ambitious.
For now, the Megapod remains a concept awaiting tangible development. The real question lies in whether Tesla can deliver a product before it faces further delays in its chip manufacturing processes.
