SDXL Turbo on NVIDIA Jetson Orin Nano Super (8 GB)

Can the NVIDIA Jetson Orin Nano Super (8 GB) run SDXL Turbo?

72 pts

Yes, it runs well

Runs well. At FP8 it needs about 4.2 GB of 6.0 GB usable, with an estimated speed of 2.1 s.

Best precision: FP8
Memory mode: Everything on GPU
Time per image: 2.1 s
Output: 512×512, 1 steps

Download SDXL Turbo

Original weights · Hugging Face

Full-precision files from Stability AI, for fine-tuning, vLLM, Transformers, Diffusers or ComfyUI.

huggingface.co/stabilityai/sdxl-turbo (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Stability AI. The weights themselves download from Hugging Face.

github.com/Stability-AI/generative-models (opens in a new tab)

git clone https://github.com/Stability-AI/generative-models

Links checked 6 days ago against Hugging Face, Ollama and GitHub.

How the A grade is scored

Every result gets up to 100 points from four parts. The points decide the letter grade.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.	17.6 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.	25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	15.0 / 15
Total	72.6 / 100	72 ptsGreat (A) · 72 of 100 points: Model quality 17.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Grade scale: S 85+ · A 72+ · B 58+ · C 42+ · D 25+ · F under 25 points.

Every precision on the NVIDIA Jetson Orin Nano Super (8 GB)

Precision	Memory needed	Available	Time per output	Memory mode	Fit	Grade · points	Download
`FP16`	2.5 GB	6.0 GB	3.1 s	Sequential CPU offload	Runs well	54 ptsUsable (C) · 54 of 100 points: Model quality 18.0/45, Memory fit 12.0/25, Speed 15.0/15, Memory use 9.6/15	Original weights (opens in a new tab)
`FP8`	4.2 GB	6.0 GB	2.1 s	Everything on GPU	Runs well	72 ptsGreat (A) · 72 of 100 points: Model quality 17.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15	Original weights (opens in a new tab)

Other hardware that runs it

73 ptsGreat (A) · 73 of 100 points: Model quality 18.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 Apple M1 (16 GB) FP16 · 3.8 s
73 ptsGreat (A) · 73 of 100 points: Model quality 18.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 Apple M4 (16 GB) FP16 · 2.6 s
72 ptsGreat (A) · 72 of 100 points: Model quality 18.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.7/15 Intel Arc B580 FP16 · 0.6 s
72 ptsGreat (A) · 72 of 100 points: Model quality 18.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.7/15 NVIDIA GeForce RTX 3060 12GB FP16 · 0.6 s
72 ptsGreat (A) · 72 of 100 points: Model quality 18.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.7/15 NVIDIA GeForce RTX 4070 SUPER FP16 · 0.3 s
69 ptsGood (B) · 69 of 100 points: Model quality 17.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.2/15 NVIDIA GeForce RTX 4060 FP8 · 0.5 s

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.