Physical AI Compute — Edge vs Cloud: Tesla FSD Chip vs Waymo Custom ASIC vs Dojo
Edge inference vs cloud training: how Tesla FSD chip, Waymo custom ASIC, and Dojo supercomputer divide AV compute across the full stack.
Edge inference vs cloud training: how Tesla FSD chip, Waymo custom ASIC, and Dojo supercomputer divide AV compute across the full stack.
Tesla bets on Dojo custom silicon at $1/FLOP target while Waymo inherits Google TPU scale; both outpace NVIDIA-dependent rivals on training iteration speed.
Waymo uses Google TPU pods and 15B simulated miles daily. Tesla built Dojo D1 for video training while running NVIDIA H100 clusters in parallel as Dojo scales.
NVIDIA B200 est. 9 exaFLOPS powers virtually all AV AI training. Tesla Dojo bets on custom silicon. Waymo uses Google TPU. Compute decides the race.
Waymo trains on Google TPU clusters. Tesla has Dojo D1 plus 6M vehicle fleet data. The training compute gap is Physical AI's hidden rate-limiter.
Tesla collects millions of FSD miles daily from 6M vehicles; Waymo runs 15B simulated miles per day. Volume vs quality defines the Physical AI pipeline race.
AV fleet charging, Dojo training power, and humanoid battery life mapped as benchmark dimensions — energy cost is underweighted in Physical AI economics.
Lidar fell 99% from $75,000 to under $500. Tesla HW4 BOM is $300-700 vs Waymo Gen 6 at $5,000-15,000; Cybercab and Gen 7 are each company hardware cost gate.
Tesla's Dojo D1 silicon powers FSD and Optimus training — the bet that faster throughput compounds into better autonomous driving.
How Tesla's custom Dojo cluster compares to renting H100/B200 cloud compute — architecture, economics, and strategic implications for FSD and Optimus.
Tesla generates more driving data per day than all robotaxi companies combined. How the FSD data flywheel compounds and why no competitor can replicate it.