Intel Core Ultra X7 358H (Panther Lake) vs NVIDIA Jetson Orin Nano Super
Split result: Intel Core Ultra X7 358H (Panther Lake) is faster in 1 and NVIDIA Jetson Orin Nano Super in 1 of 2 like-for-like comparisons.
| Intel Core Ultra X7 358H (Panther Lake) | NVIDIA Jetson Orin Nano Super | |
|---|---|---|
| Best object detection | 248 FPS C 2026-09-27 (page last edited) | 263 FPS B 2026-10-13 (page last edited) |
| Power | n/a | 25.00 W spec |
| Perf / W | n/a | 10.53 FPS/W spec |
| Price | n/a | $249 list |
| Workloads with data | 1 of 7 | 2 of 7 |
Best published object detection result per platform (may be from different like-for-like groups — see data). 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 |
|---|---|---|---|---|---|
| NVIDIA Jetson Orin Nano Super | 263 FPS inference_time · b=1 | YOLO26n INT8 · Ultralytics TensorRT · nano (YOLO-n) | B | 25.00 W spec reported in result source — spec | Ultralytics documentation (benchmark table) 2026-10-13 (page last edited) |
| Intel Core Ultra X7 358H (Panther Lake) | 248 FPS inference_time · b=1 | YOLO26n FP16 · Ultralytics + OpenVINO 2026.2.1 · nano (YOLO-n) | C | unknown power n/a no power published | Ultralytics documentation: Intel OpenVINO integration benchmark 2026-09-27 (page last edited) |
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| Intel Core Ultra X7 358H (Panther Lake) | 224 FPS inference_time · b=1 | YOLO26s INT8 · Ultralytics + OpenVINO 2026.2.1 · small (YOLO-s) | C | unknown power n/a no power published | Ultralytics documentation: Intel OpenVINO integration benchmark 2026-09-27 (page last edited) |
| NVIDIA Jetson Orin Nano Super | 146 FPS throughput · b=1 | YOLO11s (640x640) INT8 · TensorRT (ONNX export) · small (YOLO-s) | C | 25.00 W spec platform spec envelope (not measured with this result) | arXiv 2608.00927 AgriJetsonBench (abstract figures) 2026-08-02 (published) |
instance segmentation
No published instance segmentation results for Intel Core Ultra X7 358H (Panther Lake) or NVIDIA Jetson Orin Nano Super 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 Intel Core Ultra X7 358H (Panther Lake) or NVIDIA Jetson Orin Nano Super 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 Intel Core Ultra X7 358H (Panther Lake) or NVIDIA Jetson Orin Nano Super 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 NVIDIA Jetson Orin Nano Super has published semantic segmentation results.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| NVIDIA Jetson Orin Nano Super | 67.47 FPS throughput · b=1 | SegFormer-B0 (640x640) FP16 · TensorRT (ONNX export) · unknown | C | 25.00 W spec platform spec envelope (not measured with this result) | arXiv 2608.00927 AgriJetsonBench (abstract figures) 2026-08-02 (published) |
Intel Core Ultra X7 358H (Panther Lake):
No published semantic segmentation results for Intel Core Ultra X7 358H (Panther Lake) or NVIDIA Jetson Orin Nano Super 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 Intel Core Ultra X7 358H (Panther Lake) or NVIDIA Jetson Orin Nano Super 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 Intel Core Ultra X7 358H (Panther Lake) or NVIDIA Jetson Orin Nano Super 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.