Tesla's Dojo Supercomputer specializes in AI for Full-Self Driving. Its unique architecture and scalability position Tesla as a key player in AI and supercomputing.
The architectural uniqueness of Dojo is evident in its building block, the D1 chip, manufactured by TSMC using 7 nm semiconductor nodes, with a large die size of 645 mm² and 50 billion transistors and leveraging a RISC-V approach and custom instructions. The system scales by deploying multiple ExaPODs, housing up to 1,062,000 cores and reaching 20 exaflops.
This singular approach by Tesla may be a bellwether for what is to come with the wider AI domain. The norm becomes a customized integration solution to serve special purposes rather than more a general purpose approach where trained models are used as a foundation and organizations tune with private data. [LW3] It is too early to say, but this approach by Tesla will certainly only be available to a select few with the requisite deep pockets required to support it.
In summary, Dojo's advent signals a shift in the supercomputing paradigm, one that leans toward edge-driven, vertical integration, specialization, and scalable architecture. Whether it transforms supercomputing is a matter of time to tell, but what is unequivocal is its potential to provide Tesla with a technical moat that other more traditional auto manufacturers will never be able to catch up to.
By focusing on specific real-world applications and demonstrating an architecture fundamentally different from conventional designs, Tesla's Dojo emerges not merely as a tool for the company's FSD aspirations but as a landmark development in the ever-converging worlds of AI and supercomputing.[LW2]Deleted per the Forbes style notes: “We don’t include abbreviations in parentheses after full references.
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