Tesla Dojo Supercomputer: A Deep Dive into its Development, Architecture, and Future Outlook

Tesla Dojo Supercomputer: A Deep Dive into its Development, Architecture, and Future OutlookFor years, Elon Musk has spoken about Dojo, the supercomputer poised to become the core of Tesla's AI strategy. Dojo's importance is undeniable; it's considered crucial for Tesla's pursuit of Full Self-Driving (FSD) capabilities and the deployment of robotaxis

Tesla Dojo Supercomputer: A Deep Dive into its Development, Architecture, and Future Outlook

For years, Elon Musk has spoken about Dojo, the supercomputer poised to become the core of Tesla's AI strategy. Dojo's importance is undeniable; it's considered crucial for Tesla's pursuit of Full Self-Driving (FSD) capabilities and the deployment of robotaxis. However, since August 2024, news about Dojo has dwindled, with industry attention shifting towards Tesla's new AI training supercluster, Cortex. This article delves into Dojo's background, technical architecture, development history, its significance for Tesla's future strategy, and its relationship with Cortex.

Dojo's Background: The Core of Tesla's AI Strategy

Musk's ambitions extend beyond car manufacturing; he envisions Tesla as an AI company, tackling the core challenges of autonomous driving by simulating human perception. Unlike most autonomous driving companies that rely on multiple sensors (LiDAR, radar, and cameras) and high-precision maps, Tesla insists on using only cameras to collect visual data, processing it through neural networks to achieve autonomous driving. This "pure vision" approach demands immense computational power, which is the fundamental reason for Dojo's creation.

Former Tesla AI head Andrej Karpathy once likened Tesla's AI strategy to "building a synthetic animal from scratch," aiming to simulate human visual perception and decision-making processes through massive data and powerful computing capabilities. Companies like Alphabet's Waymo, employing sensor fusion and traditional machine learning methods, have already commercialized L4 autonomous driving. However, Tesla has yet to launch a fully driverless autonomous driving system.

Currently, approximately 1.8 million users pay $8,000 to $15,000 to subscribe to FSD. Tesla plans to train AI software using Dojo and deploy it to user vehicles via OTA (Over-The-Air) updates. Tesla's vast vehicle fleet provides a massive amount of driving video data, the foundation for training the FSD neural network. Tesla's core logic is: more data equals closer to achieving true full self-driving.

However, this "data-driven" strategy faces criticism. Some industry experts point out that the economic cost of training will rapidly increase, becoming a limiting factor; furthermore, simply accumulating data doesn't necessarily improve model performance; data quality and effective information extraction are key. Despite these criticisms, the data-driven trend will likely continue in the short term, and data growth inevitably necessitates higher computational power this is where Dojo comes in.

What is a Supercomputer? Dojo's Technical Architecture

Dojo is a supercomputer system specifically designed by Tesla for AI training, primarily for training the FSD neural network. Its name, derived from the Japanese martial arts training hall "dojo," reflects its core role as an AI training platform. A supercomputer consists of thousands of smaller computers called "nodes," each equipped with its own central processing unit (CPU) and graphics processing unit (GPU). The CPU handles overall management, while the GPUs handle intensive computational tasks, such as breaking down complex calculations into multiple parallel tasks.

GPUs are crucial in machine learning, especially in FSD simulation training. They also drive the development of large language models, which is why the rise of generative AI has made Nvidia one of the world's most valuable technology companies. Although Tesla has its own computing hardware, it still purchases a large number of Nvidia GPUs to train AI models.

Why Does Tesla Need a Supercomputer? Dojo's Computing Core: The D1 Chip

Tesla's "pure vision" approach to autonomous driving requires a supercomputer to process massive amounts of driving data and conduct millions of simulation trainings. Tesla aims to create a digital "human visual cortex" and "brain decision-making system," requiring the storage and processing of massive amounts of video data collected from vehicles worldwide, and real-time processing at speeds matching human visual perception.

To reduce reliance on third-party chips and improve computational efficiency and reduce latency, Tesla developed the D1 chip, optimized for AI computation. Manufactured by TSMC using a 7-nanometer process, the D1 chip boasts 50 billion transistors and a die size of 645 square millimeters. Tesla claims the chip can perform computation and data transfer simultaneously, and its Instruction Set Architecture (ISA) is completely optimized for machine learning tasks.

However, the D1 chip's performance still lags behind Nvidia's A100 GPU. To enhance computational power and data throughput, Tesla integrates 25 D1 chips into a computing unit (Tile). Each Tile delivers 9 petaflops of computational performance and 36 TB/s of bandwidth.

Dojo's overall architecture employs a modular design: 6 Tiles form a Rack, 2 Racks form a Cabinet, and 10 Cabinets constitute an ExaPOD supercomputing system. Dojo's computational power will be scaled by deploying multiple ExaPODs. Furthermore, Tesla is developing the next-generation D2 chip to further improve computational efficiency and reduce latency.

What Does Dojo Mean for Tesla? Strategic Significance and Potential Returns

Mastering in-house chip production means Tesla can potentially expand AI computing power more quickly and at a lower cost in the future. This reduces Tesla's reliance on third-party chips like Nvidia's, mitigating supply chain risks and controlling costs. During the Q2 2024 earnings call, Musk stated that the demand for Nvidia hardware was "astronomically high," and Tesla was working to reduce its dependence.

Musk has stated that Dojo has the potential to rival Nvidia in the computing power race. A September 2023 Morgan Stanley report predicted that Dojo could generate additional revenue growth for Tesla through new business models such as robotaxis and AI software services, potentially adding $500 billion to the company's market capitalization.

However, the Dojo project faces challenges. The first version of Dojo is primarily used for labeling and training Tesla's computer vision data, limiting its applications. Future versions of Dojo will be more suitable for general AI training, but this requires restructuring current GPU-based AI software.

Dojo's Progress: Current Status and Future Outlook

Previous reports indicated that Tesla began Dojo production in July 2023, but Musk stated that Dojo had been "online and performing useful tasks for months." Tesla previously projected that Dojo would rank among the world's top five most powerful supercomputers by February 2024 and reach 100 ExaFLOPS of computing power by October 2024. However, Tesla has not publicly released any information to date, and whether these goals have been achieved remains questionable.

Following the Q2 2024 earnings call, Musk stated that the Tesla AI team was using Tesla HW4 AI computers (now renamed AI4), combined with Nvidia GPUs for training. He mentioned that the current training cluster includes 90,000 Nvidia H100 GPUs and 40,000 AI4 computers. Musk added, "By the end of the year, Dojo 1's training capacity will be equivalent to 8,000 H100 GPUs."

In the Q4 2024 earnings call, Dojo wasn't even mentioned. Instead, Tesla announced the completion of Cortex deployment in Q4 and stated that Cortex was key to achieving FSD V13. This suggests a potential shift in Tesla's AI strategy focus, and Dojo's progress may be slower than anticipated.

The Relationship Between Dojo and Cortex

Both Dojo and Cortex are Tesla's AI training supercomputer clusters, but their goals and focuses differ. Dojo is primarily for training FSD neural networks, while Cortex aims to address broader real-world AI problems, including FSD and Optimus robot training. Cortex boasts "massive storage capacity," indicating a potential focus on processing and analyzing large amounts of unstructured data.

Tesla's investment in both Dojo and Cortex reflects its strong emphasis on AI technology. The development of these two projects will have a profound impact on the future development of Tesla's autonomous driving and robotics technologies.

Conclusion

Dojo, Tesla's self-developed supercomputer, is a core component of its AI strategy. Although limited public information is currently available about Dojo, it holds significant importance for Tesla's achievement of full self-driving and the exploration of new business models. Dojo's future development, and its relationship with Cortex, warrant continued attention. Time will tell whether Dojo can achieve its initial ambitious goals and deliver the expected economic benefits for Tesla as its AI technology continues to advance.