NVIDIA Jetson AGX Thor vs Raspberry Pi 5 + AI HAT+ (Hailo-8)
NVIDIA Jetson AGX Thor is faster in the only like-for-like comparison (largest gap: object detection, 15.61×).
| NVIDIA Jetson AGX Thor | Raspberry Pi 5 + AI HAT+ (Hailo-8) | |
|---|---|---|
| Best object detection | 719 FPS B undated | 76.99 FPS C 2025-12 (updated) |
| Power | 130 W spec | n/a |
| Perf / W | 5.53 FPS/W spec | n/a |
| Price | $3499 list | n/a |
| Workloads with data | 2 of 7 | 3 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 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 |
| Raspberry Pi 5 + AI HAT+ (Hailo-8) | 46.08 FPS inference_time · b=1 | YOLO26n INT8 · Ultralytics Hailo HEF / HailoRT · nano (YOLO-n) | B | unknown power n/a no power published | Ultralytics docs: Hailo integration benchmark 2026 (year only) |
instance segmentation
No published instance segmentation results for NVIDIA Jetson AGX Thor or Raspberry Pi 5 + AI HAT+ (Hailo-8) 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 NVIDIA Jetson AGX Thor or Raspberry Pi 5 + AI HAT+ (Hailo-8) 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 Raspberry Pi 5 + AI HAT+ (Hailo-8) has published image classification results.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| Raspberry Pi 5 + AI HAT+ (Hailo-8) | 500 FPS inference_time · b=1 | YOLO26n-cls INT8 · Ultralytics Hailo HEF / HailoRT · nano (YOLO-n) | B | unknown power n/a no power published | Ultralytics docs: Hailo integration benchmark 2026 (year only) |
NVIDIA Jetson AGX Thor:
No published image classification results for NVIDIA Jetson AGX Thor or Raspberry Pi 5 + AI HAT+ (Hailo-8) 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 Raspberry Pi 5 + AI HAT+ (Hailo-8) has published semantic segmentation results.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| Raspberry Pi 5 + AI HAT+ (Hailo-8) | 28.74 FPS inference_time · b=1 | YOLO26n-sem INT8 · Ultralytics Hailo HEF / HailoRT · nano (YOLO-n) | B | unknown power n/a no power published | Ultralytics docs: Hailo integration benchmark 2026 (year only) |
NVIDIA Jetson AGX Thor:
No published semantic segmentation results for NVIDIA Jetson AGX Thor or Raspberry Pi 5 + AI HAT+ (Hailo-8) 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 NVIDIA Jetson AGX Thor or Raspberry Pi 5 + AI HAT+ (Hailo-8) 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 |
Raspberry Pi 5 + AI HAT+ (Hailo-8):
No published LLM generation results for Raspberry Pi 5 + AI HAT+ (Hailo-8) 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.