Google Coral Dev Board (Edge TPU) vs NVIDIA DGX B200 (8x NVIDIA B200-SXM-180GB)

Googleedge SBCNo licensed photo yet

Googledatacenter serverNo licensed photo yet

Showing the newest results per platform and source. 18 superseded older results are hidden.Showing all results, including 18 superseded by newer results from the same source.

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 DGX B200 (8x NVIDIA B200-SXM-180GB)
Best semantic segmentation	175 FPS D Older data (2020)	n/a
Power	2.00 W spec	n/a
Perf / W	87.72 FPS/W spec	n/a
Price	n/a	n/a
Workloads with data	1 of 7	1 of 7

Best published semantic segmentation result per platform. n/a = not published; never estimated.

Show data — all workloads, sources and like-for-like groups

object detection

◌

No published object detection results for Google Coral Dev Board (Edge TPU) or NVIDIA DGX B200 (8x NVIDIA B200-SXM-180GB) 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 DGX B200 (8x NVIDIA B200-SXM-180GB) 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 DGX B200 (8x NVIDIA B200-SXM-180GB) yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.

image classification

◌

No published image classification results for Google Coral Dev Board (Edge TPU) or NVIDIA DGX B200 (8x NVIDIA B200-SXM-180GB) yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.

semantic segmentation

Only Google Coral Dev Board (Edge TPU) has published semantic segmentation 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) Older data (2020)

NVIDIA DGX B200 (8x NVIDIA B200-SXM-180GB):

◌

No published semantic segmentation results for NVIDIA DGX B200 (8x NVIDIA B200-SXM-180GB) 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.

speech-to-text

Only NVIDIA DGX B200 (8x NVIDIA B200-SXM-180GB) has published speech-to-text results.

Platform	Result	Model · precision · stack	Grade	Power	Source
NVIDIA DGX B200 (8x NVIDIA B200-SXM-180GB)	49327 FPS throughput_offline · b=unknown	MLPerf whisper fp16 · TensorRT 10.14, CUDA 13.1 · unknown	A	unknown power n/a no power published	MLPerf Inference v6.0, closed division, data-centre suite 2026-04-01 (published)

Google Coral Dev Board (Edge TPU):

◌

No published speech-to-text results for Google Coral Dev Board (Edge TPU) or NVIDIA DGX B200 (8x NVIDIA B200-SXM-180GB) yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.

LLM generation

◌

No published LLM generation results for Google Coral Dev Board (Edge TPU) or NVIDIA DGX B200 (8x NVIDIA B200-SXM-180GB) yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.

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 DGX B200 (8x NVIDIA B200-SXM-180GB)
Best object detection	90.91 FPS D Older data (2019)	n/a
Power	2.00 W spec	n/a
Perf / W	45.45 FPS/W spec	n/a
Price	n/a	n/a
Workloads with data	3 of 7	1 of 7

Best published object detection result per platform. n/a = not published; never estimated.

Show data — all workloads, sources and like-for-like groups

object detection

Only Google Coral Dev Board (Edge TPU) has published object detection results.

Platform	Result	Model · precision · stack	Grade	Power	Source
Google Coral Dev Board (Edge TPU)	90.91 FPS inference_time · b=1	MobileNet v1 SSD (224x224) INT8 · TensorFlow Lite C++ benchmark (Edge TPU runtime) · mobile-class CNN	D	2.00 W spec platform spec envelope (not measured with this result)	Google Coral Edge TPU benchmarks page (Table 1) Older data (2019)

NVIDIA DGX B200 (8x NVIDIA B200-SXM-180GB):

◌

No published object detection results for NVIDIA DGX B200 (8x NVIDIA B200-SXM-180GB) 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.

instance segmentation

◌

pose estimation

◌

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)	17.86 FPS inference_time · b=1	ResNet-50 V1 (299x299) 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) Older data (2019)

NVIDIA DGX B200 (8x NVIDIA B200-SXM-180GB):

◌

No published image classification results for NVIDIA DGX B200 (8x NVIDIA B200-SXM-180GB) 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

Only Google Coral Dev Board (Edge TPU) has published semantic segmentation 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) Older data (2020)

NVIDIA DGX B200 (8x NVIDIA B200-SXM-180GB):

◌

speech-to-text

Only NVIDIA DGX B200 (8x NVIDIA B200-SXM-180GB) has published speech-to-text results.

Platform	Result	Model · precision · stack	Grade	Power	Source
NVIDIA DGX B200 (8x NVIDIA B200-SXM-180GB)	49327 FPS throughput_offline · b=unknown	MLPerf whisper fp16 · TensorRT 10.14, CUDA 13.1 · unknown	A	unknown power n/a no power published	MLPerf Inference v6.0, closed division, data-centre suite 2026-04-01 (published)

Google Coral Dev Board (Edge TPU):

◌

LLM generation

◌

How to read these numbers

Every number is labelled. Confidence: A MLPerf closed, audited · B documented third-party method · C community / review · D vendor-published, not independently verified · E estimate. Power basis: measured with the result · spec vendor spec/envelope · power n/a not published.

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.