THE VERTICAL
VS
THE PLATFORM
Dojo is not a NVIDIA competitor. It's a purpose-built chip for one problem: training FSD on 10 million miles of daily driving video. The question is whether vertical integration beats ecosystem scale. The answer is: both are winning, at different things.
D1 VS H100 — CHIP SPECS
SYSTEM-LEVEL COMPARISON
Chip → full cluster. TFLOPS = FP16 system-level estimate.
3,000 D1 chips. 1.1 EFLOP. Purpose-built for video processing.
8x H100 SXM. 32 PFLOP. General-purpose, ships to anyone.
8x H200 SXM. Same FLOPS as H100 but 141GB HBM3e memory each.
Next-gen Dojo. Announced but not yet shipped at scale.
REALITY CHECKS
Dojo handles training. Tesla still buys H100s for inference and as training overflow. Dojo is complementary, not a replacement. Tesla is one of NVIDIA's largest customers.
$1,300 per D1 chip vs $35,000 per H100. But the D1 has 5.5x fewer TFLOPS and much less memory. The per-FLOP math gets complicated quickly — and Dojo can only be used for specific video workloads.
Dojo is a vertical integration play for Tesla's specific problem — FSD video training. It's not sold externally, has no CUDA ecosystem, and cannot run general LLMs. NVIDIA has zero customer risk from Dojo.
10 million miles of driving video per day from 6M+ vehicles. Dojo is the only chip designed specifically to process that kind of data at speed. No one else has this volume of real-world physical AI training data.
Dojo trains the FSD model. But Tesla's vehicles run inference on NVIDIA chips (Drive Orin/Thor). The training vs inference distinction is critical — Tesla designed Dojo for training, not for the car.
FSD PROGRESS TIMELINE
Dojo's success isn't measured by whether it beats NVIDIA at general AI compute. It's measured by whether it trains FSD fast enough to reach full autonomy before a competitor does.
If FSD achieves full autonomy, Tesla's 6-million-vehicle fleet becomes a robotaxi network generating Uber-scale revenue with zero driver cost. That's the trillion-dollar outcome Dojo is actually pointing at — not beating NVIDIA in the data center market.