AMD Zynq UltraScale+ ZCU104 (ZU7EV) vs NVIDIA Jetson AGX Thor

FPGA / Versal coverage note: 1,243 rows come from the AMD Vitis AI 3.0/3.5 Model Zoo tables; 733 (59%) are image classification. Detection, segmentation and other workloads are thinner, and there is no LLM or speech data for these boards. All are vendor-published (grade D). FPGA / Versal overview.

AMDeval kit (adaptive SoC)No licensed photo yet

NVIDIAedge referenceNo licensed photo yet

No like-for-like comparison yet: both have published results, but model size or batch differ. Treat the numbers below as indicative only.

	AMD Zynq UltraScale+ ZCU104 (ZU7EV)	NVIDIA Jetson AGX Thor
Best object detection	463 FPS D Older data (2023)	719 FPS B 2026-10-13 (page last edited)
Power	n/a	130 W spec
Perf / W	n/a	5.53 FPS/W spec
Price	n/a	$5499 list
Workloads with data	4 of 7	2 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

⚠ MIXED COMPARISON — not like-for-like. Baseline nano (YOLO-n) at batch 1. Differs: model-size class unknown vs nano (YOLO-n); batch unknown vs 1.

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 documentation (benchmark table) 2026-10-13 (page last edited)
AMD Zynq UltraScale+ ZCU104 (ZU7EV) MIXED: model-size class unknown vs nano (YOLO-n); batch unknown vs 1	463 FPS E2E throughput, multi thread · b=unknown	pt_yolox-nano_coco_416_416_1G INT8 · Vitis AI 3.0 (VART) · unknown	D	unknown power n/a no power published	Vitis AI 3.0 Model Zoo performance table Older data (2023)

No shared like-for-like group exists for this pair.

instance segmentation

◌

No published instance segmentation results for AMD Zynq UltraScale+ ZCU104 (ZU7EV) 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

Only AMD Zynq UltraScale+ ZCU104 (ZU7EV) has published pose estimation results.

Platform	Result	Model · precision · stack	Grade	Power	Source
AMD Zynq UltraScale+ ZCU104 (ZU7EV)	372 FPS E2E throughput, multi thread · b=unknown	pt_movenet_coco_192_192_0.5G INT8 · Vitis AI 3.0 (VART) · mobile-class CNN	D	unknown power n/a no power published	Vitis AI 3.0 Model Zoo performance table Older data (2023)

NVIDIA Jetson AGX Thor:

◌

No published pose estimation results for NVIDIA Jetson AGX Thor or AMD Zynq UltraScale+ ZCU104 (ZU7EV) 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 AMD Zynq UltraScale+ ZCU104 (ZU7EV) has published image classification results.

Platform	Result	Model · precision · stack	Grade	Power	Source
AMD Zynq UltraScale+ ZCU104 (ZU7EV)	354 FPS E2E throughput, multi thread · b=unknown	pt_OFA-resnet50_imagenet_160_160_0.88_1.8G INT8 · Vitis AI 3.0 (VART) · mid-size CNN	D	unknown power n/a no power published	Vitis AI 3.0 Model Zoo performance table Older data (2023)

NVIDIA Jetson AGX Thor:

◌

No published image classification results for NVIDIA Jetson AGX Thor or AMD Zynq UltraScale+ ZCU104 (ZU7EV) 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 AMD Zynq UltraScale+ ZCU104 (ZU7EV) has published semantic segmentation results.

Platform	Result	Model · precision · stack	Grade	Power	Source
AMD Zynq UltraScale+ ZCU104 (ZU7EV)	53.02 FPS E2E throughput, multi thread · b=unknown	pt_SemanticFPN-mobilenetv2_cityscapes_512_1024_10G INT8 · Vitis AI 3.0 (VART) · mobile-class CNN	D	unknown power n/a no power published	Vitis AI 3.0 Model Zoo performance table Older data (2023)

NVIDIA Jetson AGX Thor:

◌

No published semantic segmentation results for NVIDIA Jetson AGX Thor or AMD Zynq UltraScale+ ZCU104 (ZU7EV) 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 AMD Zynq UltraScale+ ZCU104 (ZU7EV) 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	41.32 tok/s tok/s gen, concurrency 1 · b=1	Deepseek R1 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 on or before 2025-11-08

AMD Zynq UltraScale+ ZCU104 (ZU7EV):

◌

No published LLM generation results for AMD Zynq UltraScale+ ZCU104 (ZU7EV) 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

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.