Edge AI silicon. CV5 vs Jetson vs Hexagon. #
A field comparison of the three edge silicon platforms most relevant to drone autonomy, embedded vision, and on-device inference in 2026.
At a glance #
| Platform | Peak TOPS | Power envelope | Best fit |
|---|---|---|---|
| Ambarella CV5 | ~20 TOPS | Low (battery-budget) | Small drones, video-first vision, on-device 8K capture |
| Nvidia Jetson AGX Orin | ~275 TOPS | Medium to high | Industrial robotics, autonomous mobile robots, dev-friendly stack |
| Nvidia Jetson Thor | ~2070 FP4 TFLOPS | High | Industrial robotics, medical AI, edge LLM serving |
| Qualcomm Hexagon | ~40 TOPS (Ventuno Q) | Low to medium | Mobile, voice, robotics, on-device LLMs |
Ambarella CV5 #
CV5 is an imaging-first SoC. Its differentiator is a high-quality video pipeline (8K, HDR, low-light) layered with AI acceleration optimized for vision tasks. For small drones with strict battery and weight budgets, CV5 wins on power efficiency and on the quality of the underlying imaging stack. Where it loses ground is on raw AI compute for large models. CV5 powers the Antigravity A1 drone shown at CES 2026.
Nvidia Jetson AGX Orin #
Jetson AGX Orin is the workhorse of industrial robotics in 2026. Roughly 275 TOPS, mature CUDA tooling, JetPack SDK, broad model support, and a large developer ecosystem. The cost is power: AGX Orin draws meaningfully more than CV5 or Hexagon. For robotics platforms with reasonable power budgets, AGX Orin is the default.
Nvidia Jetson Thor #
Thor is the next-generation Jetson, targeted at industrial robotics, medical AI, and edge generative-AI workloads. With around 2070 FP4 TFLOPS and 128 GB of memory, Thor is capable of running meaningfully larger models on-device than Orin. Useful for humanoid robotics, multimodal perception, and on-edge LLM serving.
Qualcomm Hexagon #
Hexagon is Qualcomm’s tensor processor, integrated into Snapdragon SoCs and the standalone Arduino Ventuno Q dev board (~40 TOPS). It targets mobile, robotics, and on-device LLM workloads with low to medium power budgets. The Ventuno Q broke the developer-board market open with an 8-core ARM CPU plus Adreno GPU plus Hexagon NPU. For voice agents, on-device LLMs, and mobile robotics, Hexagon is increasingly competitive.
How to choose #
- Drone with strict battery, video-first. CV5.
- Industrial robot with reasonable power, mature tooling. Jetson AGX Orin.
- Humanoid or industrial robot needing on-device LLMs. Jetson Thor.
- Mobile, voice, or robotics with low power and modern SDK access. Qualcomm Hexagon (Snapdragon or Ventuno Q).
In practice, the right answer is workload-dependent. A drone needs different silicon than a humanoid. The TOPS number alone is not the answer. Memory bandwidth, video pipeline quality, SDK maturity, power envelope, and total cost of ownership all matter.
Related #
- GPS-denied navigation. What the silicon actually runs in a drone autonomy stack.
- The Inference Stack in 2026. Field note on the broader inference economics, including the rise of custom silicon.