Tesla’s latest Dojo 2 supercomputer chip promises to transform how the company trains its artificial intelligence systems. This powerful processor builds on the foundation of Tesla’s D1 chip, creating a massive computing machine designed specifically for AI training tasks.
The Dojo 2 system packs impressive specifications. Each core contains 1.25 megabytes of SRAM memory, providing extremely fast data access. The chips arrange 354 application cores in a special layout with 12-node blocks. Instead of traditional cache memory that most processors use, Dojo 2 accesses SRAM directly in just five cycles. This design choice makes data retrieval much faster.
Performance numbers show the chip’s true power. It can process 376 trillion operations per second for AI-specific calculations and 22 trillion for standard math operations. The system moves data at incredible speeds too. Each core can load information at 400 gigabytes per second and store it at 270 gigabytes per second. The chip’s edge bandwidth reaches 8 terabytes per second through 576 special communication channels.
The Dojo 2 chip processes 376 trillion AI operations per second with edge bandwidth reaching 8 terabytes per second.
Power consumption remains a challenge. Each D1 die uses about 400 watts of electricity, requiring advanced cooling systems in Tesla’s data centers. The company designed custom network silicon to reduce energy waste when chips talk to each other. Tesla’s water-cooled Training Tiles package 25 D1 chips in a 5×5 array, supporting 36TB/sec aggregate bandwidth while consuming 15 kilowatts.
The chip uses an interesting approach called 4-way SMT, which means each core can handle four tasks at once. Two threads process calculations while two others move data around. This keeps the chip busy and efficient. Tesla based the core design on RISC-5 framework but added custom vector units tailored for machine learning.
Tesla won’t put these chips in cars. They’re too power-hungry and complex for vehicle use. Instead, these supercomputers will train the neural networks that eventually run in Tesla’s vehicles and support X.AI projects. The company plans to expand its AI compute capacity by over 10x within the next year and a half to support both Tesla’s autonomous driving ambitions and Musk’s broader AI initiatives. The modular tile design allows Tesla to build massive supercomputers by connecting many chips together. This enhanced AI capability is also crucial for the development of Tesla’s robotaxi service, which aims to revolutionize urban transportation. With ‘tesla robotaxi technology explained,’ the company is focusing on creating a fleet of fully autonomous vehicles that can operate efficiently without a human driver. The integration of advanced neural networks will be key to achieving a seamless and safe passenger experience.
Related chips like Tesla’s FSD processor show similar design philosophies. The FSD chip achieves 121.65 trillion operations per second using three clusters of computing cores. These processors work together in Tesla’s broader AI infrastructure, pushing forward autonomous driving technology.