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.
How Tesla's custom Dojo cluster compares to renting H100/B200 cloud compute — architecture, economics, and strategic implications for FSD and Optimus.
Benchmarking the inference and training chips powering autonomous vehicles and humanoid robots — Jetson Thor, HW4, Dojo, EyeQ Ultra — through mid-2026.
Read this because Silicon diversification, not a chip win. Anthropic already runs on Nvidia, Google TPUs, and AWS Trainium — adding Maia 200 makes it the first lab spanning all four silicon families. Optionality is the moat when compute is the bottleneck.
Anthropic is in talks to run Claude inference on Microsoft's Maia 200 chips via Azure (no deal signed, per CNBC May 21) — a hedge away from Nvidia + TPUs.
Anthropic taps SpaceX Colossus 1 in Memphis for 220K+ NVIDIA GPUs going live this month, and immediately lifts peak-hours throttling on Claude Code Pro and Max.