NVIDIA DGX Spark vs NVIDIA Jetson Orin NX 16GB

NVIDIA DGX Spark desktop AI computer, gold metal-foam chassis, oblique view on a wooden table — NVIDIA DGX Spark. Photo: Daniel Lu (User:dllu), CC BY-SA 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.

NVIDIA DGX Spark is faster in all 2 like-for-like comparisons (largest gap: LLM generation, 5.49×).

	NVIDIA DGX Spark	NVIDIA Jetson Orin NX 16GB
Best object detection	990 FPS B undated	287 FPS B undated
Power	153 W spec	n/a
Perf / W	6.47 FPS/W spec	n/a
Price	$4699 list	n/a
Workloads with data	2 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 DGX Spark	990 FPS inference_time · b=1	YOLO11n FP16 · Ultralytics TensorRT · nano (YOLO-n)	B	153 W spec platform spec envelope (not measured with this result)	Ultralytics docs benchmark table undated
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 docs benchmark table undated

instance segmentation

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No published instance segmentation results for NVIDIA DGX Spark 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 DGX Spark 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) 2026-10

NVIDIA DGX Spark:

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No published image classification results for NVIDIA DGX Spark 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

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No published semantic segmentation results for NVIDIA DGX Spark 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.

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) 2026-10

NVIDIA DGX Spark:

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No published speech-to-text results for NVIDIA DGX Spark 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 DGX Spark	60.39 tok/s tg128 · b=1	Llama 2 7B Q4_0 · llama.cpp Vulkan (FA on, commit 657e011) · LLM 5–14B	C	153 W spec platform spec envelope (not measured with this result)	llama.cpp discussion #10879 Vulkan scoreboard (via GitHub API) 2026-10-10
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) undated

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.