Tesla’s fighting a hidden battle against tiny defects that could ruin its AI training. The company’s Dojo supercomputer, which trains artificial intelligence for self-driving cars, faces a major challenge. A single faulty core among millions can destroy weeks of work.
One faulty core among millions can destroy weeks of Tesla’s AI training work.
The problem’s called silent data corruption. When it happens, the computer doesn’t crash or show error messages. Instead, it quietly produces wrong results. These errors spread through the AI training like poison, creating flawed models that nobody notices until it’s too late. By then, weeks of expensive computing time are wasted.
Tesla built a special monitoring system called Stress to catch these defects. It works like a security guard, constantly checking millions of cores across the Dojo clusters. The tool runs lightweight tests in the background while the supercomputer keeps training AI models. It doesn’t need to shut anything down. This innovative approach not only enhances the reliability of Tesla’s AI systems but also allows for seamless integration of new features. As Tesla continues to evolve its technological capabilities, many are eager to hear about Tesla’s latest product announcement, which is expected to showcase further advancements in automation. This combination of robust monitoring and cutting-edge technology positions Tesla at the forefront of the automotive and AI industries.
Finding defects takes time. Some show up in seconds, while others hide for hours. The Stress tool typically catches problems after a core processes between one and 100 gigabytes of data. Once it spots a bad core, the system disables just that one piece. Each D1 chip can lose a few cores and still work fine. The Stress tool’s periodic XOR integration into SRAM boosted defect detection probability by 10x without hurting performance.
The monitoring revealed more than just hardware problems. Tesla’s engineers uncovered rare design flaws they could fix with software updates. They also found and corrected several low-level software issues during implementation. The SDC issues stem from both manufacturing defects and component aging over time. Like Tesla’s vehicle electric powertrains that feature fewer moving parts to reduce wear and tear, the company’s supercomputing infrastructure is designed for maximum durability.
Tesla’s defect rates match what Google and Meta report for their systems. It’s a common problem in the supercomputing world. The company’s now using Stress on all operational Dojo clusters to watch for problems during actual AI training runs.
Looking ahead, Tesla’s preparing Dojo 2 for launch later in 2025. The new version includes improvements based on lessons learned from the current system. Dojo 3‘s already in development too. Tesla believes the third version will be notably better, following the industry rule that major breakthroughs often come on the third try.
The battle against defects continues, but Tesla’s tools and techniques keep improving to protect its essential AI training operations.