What is Edge Compute?
Edge compute means running computation — especially AI inference — on the device itself (the “edge” of the network) rather than sending data to a remote cloud server and waiting for a response.
Why It Matters for Robots
A warehouse robot navigating around humans can’t wait 200ms for a cloud API to decide “don’t hit that person.” A Combat robot operating in a jammed environment can’t rely on a 5G connection. Edge compute is the difference between:
- Autonomy: Robot decides in real time, no network needed
- teleoperated-vs-autonomous: Human decides, but the robot still needs local processing to execute smoothly
- Remote control: Pure lag — not viable for dynamic environments
The Hardware
| Platform | AI Performance | Use Case |
|---|---|---|
| nvidia-jetson | 1,035 FP8 TFLOPs | Humanoid robots, high-complexity perception |
| NVIDIA Jetson AGX Orin | 275 INT8 TOPS | Drones, warehouse robots |
| Qualcomm RB3 Gen 2 | 12 TOPS | Consumer robots, low-power devices |
| Intel Movidius Myriad X | 1 TOPS | Simple vision tasks |
The Trade-Offs
Power vs. Performance: More compute = more heat = bigger battery. A humanoid carrying a Jetson Thor and its cooling system adds kilograms of weight.
Cost: Jetson Thor retails at ~20,000 robot’s BOM.
Security: Edge devices can’t be air-gapped easily. If the robot is compromised, the attacker has physical access — a nightmare scenario for military deployments.
Cloud-Edge Hybrid
Most practical systems use a hybrid: edge for real-time safety-critical decisions (collision avoidance, balance), cloud for training, fleet analytics, and non-time-sensitive planning.
The Bottom Line
Edge compute is non-negotiable for true autonomy. The question isn’t whether to use it — it’s which platform, how much power it draws, and what happens when the network cuts out.