Dell Pro Max with GB10 vs Google Coral Dev Board (Edge TPU)
No like-for-like comparison yet: both have published results, but model size or batch differ. Treat the numbers below as indicative only.
| Dell Pro Max with GB10 | Google Coral Dev Board (Edge TPU) | |
|---|---|---|
| Best object detection | 78.73 FPS A 2026-09-16 (published) | n/a |
| Power | n/a | n/a |
| Perf / W | n/a | n/a |
| Price | n/a | n/a |
| Workloads with data | 2 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 Dell Pro Max with GB10 has published object detection results.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| Dell Pro Max with GB10 | 78.73 FPS throughput_offline · b=unknown | MLPerf yolo-95 fp4 · CUDA 13.0 · unknown | A | unknown power n/a no power published | MLPerf Inference v6.1, closed division, edge suite 2026-09-16 (published) |
Google Coral Dev Board (Edge TPU):
No published object detection results for Google Coral Dev Board (Edge TPU) or Dell Pro Max with GB10 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 Dell Pro Max with GB10 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.
pose estimation
No published pose estimation results for Dell Pro Max with GB10 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.
image classification
No published image classification results for Dell Pro Max with GB10 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) |
Dell Pro Max with GB10:
No published semantic segmentation results for Dell Pro Max with GB10 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
No published speech-to-text results for Dell Pro Max with GB10 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.
LLM generation
Only Dell Pro Max with GB10 has published LLM generation results.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| Dell Pro Max with GB10 | 23.92 tok/s generation_throughput · b=1 | DeepSeek R1 14b as in linked issue (Ollama default tags are 4-bit) · Ollama / llama.cpp (see linked issue) · LLM 5–14B | C | 138 W measured reported in result source — measured | Jeff Geerling ai-benchmarks README (Findings tables) 2026-09-22 (page last edited) |
Google Coral Dev Board (Edge TPU):
No published LLM generation results for Google Coral Dev Board (Edge TPU) or Dell Pro Max with GB10 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.
| Dell Pro Max with GB10 | Google Coral Dev Board (Edge TPU) | |
|---|---|---|
| Best object detection | 78.73 FPS A 2026-09-16 (published) | 90.91 FPS D Older data (2019) |
| Power | n/a | 2.00 W spec |
| Perf / W | n/a | 45.45 FPS/W spec |
| Price | n/a | n/a |
| Workloads with data | 2 of 7 | 3 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
| 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) |
| Dell Pro Max with GB10 MIXED: model-size class unknown vs mobile-class CNN; batch unknown vs 1 | 78.73 FPS throughput_offline · b=unknown | MLPerf yolo-95 fp4 · CUDA 13.0 · unknown | A | unknown power n/a no power published | MLPerf Inference v6.1, closed division, edge suite 2026-09-16 (published) |
No shared like-for-like group exists for this pair.
instance segmentation
No published instance segmentation results for Dell Pro Max with GB10 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.
pose estimation
No published pose estimation results for Dell Pro Max with GB10 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.
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) |
Dell Pro Max with GB10:
No published image classification results for Dell Pro Max with GB10 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) |
Dell Pro Max with GB10:
No published semantic segmentation results for Dell Pro Max with GB10 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
No published speech-to-text results for Dell Pro Max with GB10 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.
LLM generation
Only Dell Pro Max with GB10 has published LLM generation results.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| Dell Pro Max with GB10 | 23.92 tok/s generation_throughput · b=1 | DeepSeek R1 14b as in linked issue (Ollama default tags are 4-bit) · Ollama / llama.cpp (see linked issue) · LLM 5–14B | C | 138 W measured reported in result source — measured | Jeff Geerling ai-benchmarks README (Findings tables) 2026-09-22 (page last edited) |
Google Coral Dev Board (Edge TPU):
No published LLM generation results for Google Coral Dev Board (Edge TPU) or Dell Pro Max with GB10 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.