Inference Case Study: Tesla
Tesla is a unique example of an organization that spans the entire AI inference spectrum — from massive training clusters to real-time inference in millions of vehicles and emerging humanoid robots. Unlike most enterprises that outsource inference to hyperscale APIs, Tesla vertically integrates the full pipeline: training > OTA model distribution > on-device inference > telemetry feedback > retraining. This closed loop enables continuous improvement of autonomous driving and robotics systems.
Overview
- Purpose: Deliver safe, continuously improving autonomous driving (robotaxis, FSD) and humanoid robotics via real-time inference.
- Scale: Billions of training video clips, millions of cars running inference, early humanoid prototypes.
- Characteristics: High-volume training + OTA fleet deployment + low-latency on-device inference + telemetry feedback.
- Comparison: Tesla is the only automaker that has attempted a full closed-loop AI factory model across both mobility and humanoids.
Inference Pipeline
| Stage | Location | Function | Notes |
|---|---|---|---|
| Training | Cortex (Austin), Colossus (Memphis) | GPU/Dojo clusters train on billions of fleet videos | Dojo disbanded 2025; shifted to GPU-heavy clusters |
| OTA Distribution | Tesla Cloud + Enterprise IT | Model updates distributed to cars/robots over-the-air | Continuous integration into fleet |
| On-Device Inference | Tesla FSD Computer (HW3–HW5, AI5) | Real-time perception, planning, actuation inference | Sub-50 ms latency required for driving |
| Telemetry Feedback | Fleet uploads ? Data Centers | Video clips, edge cases, disengagements collected | Closed loop for retraining datasets |
| Humanoid Inference | Optimus Gen 3 | Vision, motion planning, speech models run locally | Uses FSD-derived inference stack |
Deployment Contexts
| Context | How Tesla Fits | Notes |
|---|---|---|
| Hyperscale | Cortex DC for training + inference pre-processing | Acts as central “AI factory” for fleet models |
| On-Prem | Tesla’s vertically integrated data centers | Closed loop — no dependence on AWS/Azure/GCP |
| Edge | Fleet vehicles function as roaming edge inference nodes | Each car runs local inference + V2X comms |
| Devices | HW5/AI5 chips in cars; Optimus robot “brain” | Dedicated silicon for ultra-low latency decisions |
Key Challenges
- Energy Demand: Cortex/Colossus clusters draw tens of MW, raising sustainability questions.
- Dojo Pivot: Original in-house training silicon project (Dojo) shut down in 2025, replaced by GPU reliance.
- OTA Risk: Updating live fleet AI models requires strong validation and rollback safety nets.
- Edge Case Volume: Billions of real-world scenarios must be processed for robust driving inference.
- Regulatory Scrutiny: FSD inference systems are under heavy regulatory and safety oversight worldwide.
Notable Elements
- Cortex (Austin): Current flagship training + inference staging cluster.
- Colossus (Memphis): Scale-up follow-on facility; future unclear post-Dojo shutdown.
- Tesla FSD HW5 / AI5: Inference chips running onboard cars for autonomy.
- Optimus: Humanoid robot using modified FSD inference stack.
Future Outlook
- AI Factory Model: Tesla will continue refining its closed-loop inference pipeline.
- Vertical Integration: Less focus on custom silicon (Dojo) — more on software + GPU efficiency.
- Humanoids: Optimus will likely become a second “inference at scale” case alongside FSD fleet.
- Federated Potential: Cars as distributed inference + training agents in future architectures.
FAQ
- How is Tesla different from other automakers? Tesla vertically integrates training clusters, OTA distribution, and on-device inference.
- Did Dojo run inference? No, Dojo was designed for training; inference happens in FSD computers.
- Why on-device inference in cars? Latency (sub-50 ms) and safety requirements prevent reliance on external DCs.
- How does Optimus fit? Optimus reuses Tesla’s FSD inference stack for robotics.
- What’s next? Scaling inference across fleets + robots with GPU-based training backends.