NVIDIA Jetson AGX Orin 64GB vs NVIDIA Jetson Orin NX 16GB

NVIDIA Jetson AGX Orin developer kit with its retail box, on a black background — NVIDIA Jetson AGX Orin developer kit. Photo: Auledas, CC BY 4.0, via Wikimedia Commons.

NVIDIA Jetson Orin NX module seated on a carrier motherboard, close-up at Computex 2025 — NVIDIA Jetson Orin NX module on a carrier board. Photo: 4300streetcar, CC BY 4.0, via Wikimedia Commons.

Showing the newest results per platform and source. 12 superseded older results are hidden.Showing all results, including 12 superseded by newer results from the same source.

NVIDIA Jetson AGX Orin 64GB is faster in all 5 like-for-like comparisons (largest gap: LLM generation, 4.20×).

	NVIDIA Jetson AGX Orin 64GB	NVIDIA Jetson Orin NX 16GB
Best object detection	435 FPS B 2026-10-13 (page last edited)	287 FPS B 2026-10-13 (page last edited)
Power	65.00 W spec	n/a
Perf / W	6.69 FPS/W spec	n/a
Price	$3499 list	n/a
Workloads with data	3 of 7	4 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

✓ Like-for-like: model-size class nano (YOLO-n), batch 1. Model, precision and stack may still differ.

Platform	Result	Model · precision · stack	Grade	Power	Source
NVIDIA Jetson AGX Orin 64GB	435 FPS inference_time · b=1	YOLO26n INT8 · Ultralytics TensorRT · nano (YOLO-n)	B	65.00 W spec platform spec envelope (not measured with this result)	Ultralytics documentation (benchmark table) 2026-10-13 (page last edited)
NVIDIA Jetson Orin NX 16GB	287 FPS inference_time · b=1	YOLO26n INT8 · Ultralytics TensorRT · nano (YOLO-n)	B	unknown power n/a no power published	Ultralytics documentation (benchmark table) 2026-10-13 (page last edited)

instance segmentation

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No published instance segmentation results for NVIDIA Jetson AGX Orin 64GB or NVIDIA Jetson Orin NX 16GB yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.

pose estimation

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No published pose estimation results for NVIDIA Jetson AGX Orin 64GB or NVIDIA Jetson Orin NX 16GB 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 NVIDIA Jetson Orin NX 16GB has published image classification results.

Platform	Result	Model · precision · stack	Grade	Power	Source
NVIDIA Jetson Orin NX 16GB	2641 FPS throughput_offline · b=unknown	MLPerf ResNet INT8/FP16 (MLPerf closed) · TensorRT (MLPerf Inference, older round) · mid-size CNN	D	unknown power n/a no power published	NVIDIA Jetson Benchmarks (MLPerf v3.1 results table) Older data (2023)

NVIDIA Jetson AGX Orin 64GB:

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No published image classification results for NVIDIA Jetson AGX Orin 64GB or NVIDIA Jetson Orin NX 16GB 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 AGX Orin 64GB has published semantic segmentation results.

Platform	Result	Model · precision · stack	Grade	Power	Source
NVIDIA Jetson AGX Orin 64GB	104 FPS throughput · b=1	SegFormer-B0 (640x640) FP16 · TensorRT (ONNX export) · unknown	C	65.00 W spec platform spec envelope (not measured with this result)	arXiv 2608.00927 AgriJetsonBench (abstract figures) 2026-08-02 (published)

NVIDIA Jetson Orin NX 16GB:

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No published semantic segmentation results for NVIDIA Jetson Orin NX 16GB or NVIDIA Jetson AGX Orin 64GB 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 Jetson Orin NX 16GB has published speech-to-text results.

Platform	Result	Model · precision · stack	Grade	Power	Source
NVIDIA Jetson Orin NX 16GB	432 FPS throughput_offline · b=unknown	MLPerf RNN-T INT8/FP16 (MLPerf closed) · TensorRT (MLPerf Inference, older round) · RNN-T	D	unknown power n/a no power published	NVIDIA Jetson Benchmarks (MLPerf v3.1 results table) Older data (2023)

NVIDIA Jetson AGX Orin 64GB:

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No published speech-to-text results for NVIDIA Jetson AGX Orin 64GB or NVIDIA Jetson Orin NX 16GB yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.

LLM generation

✓ Like-for-like: model-size class LLM 5–14B, batch 1. Model, precision and stack may still differ.

Platform	Result	Model · precision · stack	Grade	Power	Source
NVIDIA Jetson AGX Orin 64GB	30.00 tok/s generation_throughput · b=1	Qwen 3 8B W4A16 · vLLM · LLM 5–14B	C	65.00 W spec platform spec envelope (not measured with this result)	NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited)
NVIDIA Jetson Orin NX 16GB	11.00 tok/s generation_throughput · b=1	Nemotron Nano 9B V2 W4A16 · vLLM · LLM 5–14B	C	unknown power n/a no power published	NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited)

✓ Like-for-like: model-size class LLM 5–14B, batch 8. Model, precision and stack may still differ.

Platform	Result	Model · precision · stack	Grade	Power	Source
NVIDIA Jetson AGX Orin 64GB	157 tok/s generation_throughput · b=8	Qwen 3 8B W4A16 · vLLM · LLM 5–14B	C	65.00 W spec platform spec envelope (not measured with this result)	NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited)
NVIDIA Jetson Orin NX 16GB	60.00 tok/s generation_throughput · b=8	Nemotron Nano 9B V2 W4A16 · vLLM · LLM 5–14B	C	unknown power n/a no power published	NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited)

✓ Like-for-like: model-size class LLM ≤4B, batch 1. Model, precision and stack may still differ.

Platform	Result	Model · precision · stack	Grade	Power	Source
NVIDIA Jetson AGX Orin 64GB	60.00 tok/s generation_throughput · b=1	Qwen3-VL-2B W4A16 · vLLM · LLM ≤4B	C	65.00 W spec platform spec envelope (not measured with this result)	NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited)
NVIDIA Jetson Orin NX 16GB	25.00 tok/s generation_throughput · b=1	Cosmos Reasoning 2 2B W4A16 · vLLM · LLM ≤4B	C	unknown power n/a no power published	NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited)

✓ Like-for-like: model-size class LLM ≤4B, batch 8. Model, precision and stack may still differ.

Platform	Result	Model · precision · stack	Grade	Power	Source
NVIDIA Jetson AGX Orin 64GB	273 tok/s generation_throughput · b=8	Cosmos Reasoning 2 2B W4A16 · vLLM · LLM ≤4B	C	65.00 W spec platform spec envelope (not measured with this result)	NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited)
NVIDIA Jetson Orin NX 16GB	65.00 tok/s generation_throughput · b=8	Cosmos Reasoning 2 2B W4A16 · vLLM · LLM ≤4B	C	unknown power n/a no power published	NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited)

NVIDIA Jetson AGX Orin 64GB is faster in all 8 like-for-like comparisons (largest gap: LLM generation, 4.20×).

	NVIDIA Jetson AGX Orin 64GB	NVIDIA Jetson Orin NX 16GB
Best object detection	435 FPS B 2026-10-13 (page last edited)	287 FPS B 2026-10-13 (page last edited)
Power	65.00 W spec	n/a
Perf / W	6.69 FPS/W spec	n/a
Price	$3499 list	n/a
Workloads with data	5 of 7	4 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

✓ Like-for-like: model-size class nano (YOLO-n), batch 1. Model, precision and stack may still differ.

Platform	Result	Model · precision · stack	Grade	Power	Source
NVIDIA Jetson AGX Orin 64GB	435 FPS inference_time · b=1	YOLO26n INT8 · Ultralytics TensorRT · nano (YOLO-n)	B	65.00 W spec platform spec envelope (not measured with this result)	Ultralytics documentation (benchmark table) 2026-10-13 (page last edited)
NVIDIA Jetson Orin NX 16GB	287 FPS inference_time · b=1	YOLO26n INT8 · Ultralytics TensorRT · nano (YOLO-n)	B	unknown power n/a no power published	Ultralytics documentation (benchmark table) 2026-10-13 (page last edited)

✓ Like-for-like: model-size class mid-size CNN, batch unknown (size or batch not stated by source — unverified). Model, precision and stack may still differ.

Platform	Result	Model · precision · stack	Grade	Power	Source
NVIDIA Jetson AGX Orin 64GB	149 FPS throughput_offline · b=unknown	MLPerf Retinanet INT8/FP16 (MLPerf closed) · TensorRT (MLPerf Inference, older round) · mid-size CNN	D	65.00 W spec platform spec envelope (not measured with this result)	NVIDIA Jetson Benchmarks (MLPerf v3.1 results table) Older data (2023)
NVIDIA Jetson Orin NX 16GB	66.50 FPS throughput_offline · b=unknown	MLPerf Retinanet INT8/FP16 (MLPerf closed) · TensorRT (MLPerf Inference, older round) · mid-size CNN	D	unknown power n/a no power published	NVIDIA Jetson Benchmarks (MLPerf v3.1 results table) Older data (2023)

instance segmentation

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pose estimation

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image classification

✓ Like-for-like: model-size class mid-size CNN, batch unknown (size or batch not stated by source — unverified). Model, precision and stack may still differ.

Platform	Result	Model · precision · stack	Grade	Power	Source
NVIDIA Jetson AGX Orin 64GB	6424 FPS throughput_offline · b=unknown	MLPerf ResNet INT8/FP16 (MLPerf closed) · TensorRT (MLPerf Inference, older round) · mid-size CNN	D	65.00 W spec platform spec envelope (not measured with this result)	NVIDIA Jetson Benchmarks (MLPerf v3.1 results table) Older data (2023)
NVIDIA Jetson Orin NX 16GB	2641 FPS throughput_offline · b=unknown	MLPerf ResNet INT8/FP16 (MLPerf closed) · TensorRT (MLPerf Inference, older round) · mid-size CNN	D	unknown power n/a no power published	NVIDIA Jetson Benchmarks (MLPerf v3.1 results table) Older data (2023)

semantic segmentation

Only NVIDIA Jetson AGX Orin 64GB has published semantic segmentation results.

Platform	Result	Model · precision · stack	Grade	Power	Source
NVIDIA Jetson AGX Orin 64GB	104 FPS throughput · b=1	SegFormer-B0 (640x640) FP16 · TensorRT (ONNX export) · unknown	C	65.00 W spec platform spec envelope (not measured with this result)	arXiv 2608.00927 AgriJetsonBench (abstract figures) 2026-08-02 (published)

NVIDIA Jetson Orin NX 16GB:

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speech-to-text

✓ Like-for-like: model-size class RNN-T, batch unknown (size or batch not stated by source — unverified). Model, precision and stack may still differ.

Platform	Result	Model · precision · stack	Grade	Power	Source
NVIDIA Jetson AGX Orin 64GB	1170 FPS throughput_offline · b=unknown	MLPerf RNN-T INT8/FP16 (MLPerf closed) · TensorRT (MLPerf Inference, older round) · RNN-T	D	65.00 W spec platform spec envelope (not measured with this result)	NVIDIA Jetson Benchmarks (MLPerf v3.1 results table) Older data (2023)
NVIDIA Jetson Orin NX 16GB	432 FPS throughput_offline · b=unknown	MLPerf RNN-T INT8/FP16 (MLPerf closed) · TensorRT (MLPerf Inference, older round) · RNN-T	D	unknown power n/a no power published	NVIDIA Jetson Benchmarks (MLPerf v3.1 results table) Older data (2023)

LLM generation

✓ Like-for-like: model-size class LLM 5–14B, batch 1. Model, precision and stack may still differ.

Platform	Result	Model · precision · stack	Grade	Power	Source
NVIDIA Jetson AGX Orin 64GB	30.00 tok/s generation_throughput · b=1	Qwen 3 8B W4A16 · vLLM · LLM 5–14B	C	65.00 W spec platform spec envelope (not measured with this result)	NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited)
NVIDIA Jetson Orin NX 16GB	11.00 tok/s generation_throughput · b=1	Nemotron Nano 9B V2 W4A16 · vLLM · LLM 5–14B	C	unknown power n/a no power published	NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited)

✓ Like-for-like: model-size class LLM 5–14B, batch 8. Model, precision and stack may still differ.

Platform	Result	Model · precision · stack	Grade	Power	Source
NVIDIA Jetson AGX Orin 64GB	157 tok/s generation_throughput · b=8	Qwen 3 8B W4A16 · vLLM · LLM 5–14B	C	65.00 W spec platform spec envelope (not measured with this result)	NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited)
NVIDIA Jetson Orin NX 16GB	60.00 tok/s generation_throughput · b=8	Nemotron Nano 9B V2 W4A16 · vLLM · LLM 5–14B	C	unknown power n/a no power published	NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited)

✓ Like-for-like: model-size class LLM ≤4B, batch 1. Model, precision and stack may still differ.

Platform	Result	Model · precision · stack	Grade	Power	Source
NVIDIA Jetson AGX Orin 64GB	60.00 tok/s generation_throughput · b=1	Qwen3-VL-2B W4A16 · vLLM · LLM ≤4B	C	65.00 W spec platform spec envelope (not measured with this result)	NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited)
NVIDIA Jetson Orin NX 16GB	25.00 tok/s generation_throughput · b=1	Cosmos Reasoning 2 2B W4A16 · vLLM · LLM ≤4B	C	unknown power n/a no power published	NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited)

✓ Like-for-like: model-size class LLM ≤4B, batch 8. Model, precision and stack may still differ.

Platform	Result	Model · precision · stack	Grade	Power	Source
NVIDIA Jetson AGX Orin 64GB	273 tok/s generation_throughput · b=8	Cosmos Reasoning 2 2B W4A16 · vLLM · LLM ≤4B	C	65.00 W spec platform spec envelope (not measured with this result)	NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited)
NVIDIA Jetson Orin NX 16GB	65.00 tok/s generation_throughput · b=8	Cosmos Reasoning 2 2B W4A16 · vLLM · LLM ≤4B	C	unknown power n/a no power published	NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited)

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.