Google Coral Dev Board (Edge TPU) vs NVIDIA Jetson AGX Thor
No like-for-like comparison yet: both have published results, but model size or batch differ. Treat the numbers below as indicative only.
| Google Coral Dev Board (Edge TPU) | NVIDIA Jetson AGX Thor | |
|---|---|---|
| Best image classification | 500 FPS D undated | n/a |
| Power | 2.00 W spec | n/a |
| Perf / W | 250 FPS/W spec | n/a |
| Price | n/a | $3499 list |
| Workloads with data | 1 of 7 | 2 of 7 |
Best published image classification result per platform. n/a = not published; never estimated.
Show data — all workloads, sources and like-for-like groups
object detection
Only NVIDIA Jetson AGX Thor has published object detection results.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| NVIDIA Jetson AGX Thor | 719 FPS inference_time · b=1 | YOLO26n FP16 · Ultralytics TensorRT · nano (YOLO-n) | B | 130 W spec platform spec envelope (not measured with this result) | Ultralytics docs benchmark table undated |
Google Coral Dev Board (Edge TPU):
No published object detection results for Google Coral Dev Board (Edge TPU) or NVIDIA Jetson AGX Thor yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.
instance segmentation
No published instance segmentation results for Google Coral Dev Board (Edge TPU) or NVIDIA Jetson AGX Thor yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.
pose estimation
No published pose estimation results for Google Coral Dev Board (Edge TPU) or NVIDIA Jetson AGX Thor yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.
image classification
Only Google Coral Dev Board (Edge TPU) has published image classification results.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| Google Coral Dev Board (Edge TPU) | 175 FPS inference_time · b=1 | Unet Mv2 (128x128) INT8 · TensorFlow Lite C++ benchmark (Edge TPU runtime) · mid-size CNN | D | 2.00 W spec platform spec envelope (not measured with this result) | Google Coral Edge TPU benchmarks page (Table 1) undated |
NVIDIA Jetson AGX Thor:
No published image classification results for NVIDIA Jetson AGX Thor or Google Coral Dev Board (Edge TPU) yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.
semantic segmentation
No published semantic segmentation results for Google Coral Dev Board (Edge TPU) or NVIDIA Jetson AGX Thor yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.
speech-to-text
No published speech-to-text results for Google Coral Dev Board (Edge TPU) or NVIDIA Jetson AGX Thor yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.
LLM generation
Only NVIDIA Jetson AGX Thor has published LLM generation results.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| NVIDIA Jetson AGX Thor | 45.00 tok/s tok/s gen, concurrency 1 · b=1 | Qwen2.5-VL 7B n/s on page · NVIDIA (vLLM/TensorRT-LLM per page) · LLM 5–14B | D | 130 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson Benchmarks undated |
Google Coral Dev Board (Edge TPU):
No published LLM generation results for Google Coral Dev Board (Edge TPU) or NVIDIA Jetson AGX Thor yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.
How to read these numbers
Machine-readable: /find.json (see llms.txt); full tables are in “Show data”. Confidence grades: A audited MLPerf closed · B documented third-party · C community · D vendor-published · E estimate. Unknown values are shown as unknown, never estimated. Generated 2026-10-16 from public sources.