Benchmarks

Below are AI inferencing benchmarks for Jetson Orin Nano Super and Jetson AGX Orin .

Jetson Orin Nano Super

LLM / SLM Vision / Language Models Vision Transformers

Model	Jetson Orin Nano (original)	Jetson Orin Nano Super	Perf Gain (X)
Llama 3.1 8B	14	19.14	1.37
Llama 3.2 3B	27.7	43.07	1.55
Qwen2.5 7B	14.2	21.75	1.53
Gemma 2 2B	21.5	34.97	1.63
Gemma 2 9B	7.2	9.21	1.28
Phi 3.5 3B	24.7	38.1	1.54
SmolLM2	41	64.5	1.57

For running these benchmarks, this script will launch a series of containers that download/build/run the models with MLC and INT4 quantization.

git clone https://github.com/dusty-nv/jetson-containers
bash jetson-containers/install.sh
bash jetson-containers/packages/llm/mlc/benchmarks.sh

Model	Jetson Orin Nano (original)	Jetson Orin Nano Super	Perf Gain (X)
VILA 1.5 3B	0.7	1.06	1.51
VILA 1.5 8B	0.574	0.83	1.45
LLAVA 1.6 7B	0.412	0.57	1.38
Qwen2 VL 2B	2.8	4.4	1.57
InternVL2.5 4B	2.5	5.1	2.04
PaliGemma2 3B	13.7	21.6	1.58
SmolVLM 2B	8.1	12.9	1.59

Model	Jetson Orin Nano (original)	Jetson Orin Nano Super	Perf Gain (X)
clip-vit-base-patch32	196	314	1.60
clip-vit-base-patch16	95	161	1.69
DINOv2-base-patch14	75	126	1.68
SAM2 base	4.42	6.34	1.43
Grounding DINO	4.11	6.23	1.52
vit-base-patch16-224	98	158	1.61
vit-base-patch32-224	171	273	1.60

Jetson AGX Orin

Large Language Models (LLM) Small Language Models (SLM) Vision Language Models (VLM) Vision Transformers (ViT) Stable Diffusion Riva Vector Database

For running LLM benchmarks, see the MLC container documentation.

Small language models are generally defined as having fewer than 7B parameters (Llama-7B shown for reference)
For more data and info about running these models, see the SLM tutorial and MLC container documentation.

This measures the end-to-end pipeline performance for continuous streaming like with Live Llava .
For more data and info about running these models, see the NanoVLM tutorial.

VIT performance data from [1] [2] [3]

For running Riva benchmarks, see ASR Performance and TTS Performance .

For running vector database benchmarks, see the NanoDB container documentation.