NVIDIA Jetson Orin Nano Super Developer Kit

Hardware · NVIDIA

What AI models can the NVIDIA Jetson Orin Nano Super (8 GB) run?

Unified memory
8.0 GB
Usable for models
6.0 GB
Memory bandwidth
102 GB/s
Runs models on
GPU (CUDA)
Released
2024
288 / 622 LLMs run Best: Nemotron Labs Dory 1.0 4B 43 / 61 Image models run Best: FLUX.2 [klein] 4B 3 / 43 Video models run Best: AnimateDiff v3 (SD 1.5) 59 / 70 Decision models run Best: GLiNER2.5 Decide

Models run on the GPU (CUDA) here, sharing the board’s memory with the system. We plan around 6.0 GB of the 8.0 GB (what Linux leaves free for a model). Ampere GPU with 1,024 CUDA cores and 32 Tensor Cores: 67 TOPS (INT8, sparse) in Super mode.

More about this hardware

MakerNVIDIA
TypeSingle-board computer or edge AI board
GPU share of memory75% (what the GPU can typically be given for a model)
ChipNVIDIA Jetson Orin Nano Super
AI acceleratorAmpere GPU with 1,024 CUDA cores and 32 Tensor Cores: 67 TOPS (INT8, sparse) in Super mode.
Boards with this chipJetson Orin Nano Super Developer Kit · Jetson Orin Nano 8GB module (Super mode)
Memory type8 GB LPDDR5, 128-bit (102 GB/s in Super mode, 68 GB/s before JetPack 6.2)
Bandwidth figure102.0 GB/s, as the maker states it
Note8 GB shared with Ubuntu desktop; ~6 GB realistically free for models (estimate).
Specificationswww.nvidia.com

Apps that run models well on the NVIDIA Jetson Orin Nano Super (8 GB)

Linux on ARM boards

  1. OllamaOne command with Ollama’s own build of this model.
  2. LM StudioPoint-and-click: search, download and chat in one window.
  3. llama.cppUsually the first to support new model architectures.
  4. Docker Model RunnerHandy if you already work in Docker.

For a typical model that fits in memory. Each model page picks the best app for that model on this hardware. All apps

1 product with the NVIDIA Jetson Orin Nano Super (8 GB)

Best LLMs for the NVIDIA Jetson Orin Nano Super (8 GB)

Quality Model Quant Memory Speed Runs
64Good quality (B) · 64 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. Nemotron Labs Dory 1.0 4BNVIDIA Q4_K_M 4.0 GB 31.5 tok/s Runs great
60Good quality (B) · 60 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. Gemma 4 E2BGoogle Q5_K_M 4.8 GB 45.0 tok/s Runs great
62Good quality (B) · 62 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. Spark-X2.6 4BSparkLLM Q4_K_M 3.9 GB 30.5 tok/s Runs great
64Good quality (B) · 64 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. Nanbeige4.2 3BBOSS Zhipin (Nanbeige) Q4_K_M 5.1 GB 30.0 tok/s Runs welltight fit
58Good quality (B) · 58 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. Nemotron 3.5 Bolt E4BNVIDIA Q3_K_M 5.1 GB 36.0 tok/s Runs welltight fit
57Good quality (B) · 57 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. TMax 4BAi2 Q4_K_M 3.9 GB 29.7 tok/s Runs well
57Good quality (B) · 57 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking. Gemma 4 E4BGoogle Q3_K_M 5.1 GB 36.0 tok/s Runs welltight fit
58Good quality (B) · 58 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking. Qwen3.5 4BAlibaba Q4_K_M 4.2 GB 26.9 tok/s Runs well
58Good quality (B) · 58 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking. MiniCPM5 2BOpenBMB Q5_K_M 3.2 GB 41.0 tok/s Runs great
65Good quality (B) · 65 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking. MiMo V2.6 Distill Qwen 9BXiaomi Q3_K_M 6.0 GB 17.2 tok/s Runs welltight fit
57Good quality (B) · 57 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking. Agents-A1 4BInternScience Q4_K_M 4.1 GB 27.6 tok/s Runs well
55Good quality (B) · 55 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking. Fara 1.5 4BMicrosoft Q4_K_M 4.1 GB 27.6 tok/s Runs well

Best Image models for the NVIDIA Jetson Orin Nano Super (8 GB)

Quality Model Quant Memory Speed Runs
72Great quality (A) · 72 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking. FLUX.2 [klein] 4BBlack Forest Labs Q4 GGUF 4.6 GB 20 s Runs wellencoder unloaded after use
52Fair quality (C) · 52 of 100: how capable the model is at FP8, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. SDXL-Lightning (4-step)ByteDance FP8 5.6 GB 9.9 s Runs welltight fit
84Great quality (A) · 84 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. FLUX 3 Image KleinBlack Forest Labs Q4 GGUF 5.7 GB 1.9 min Slowtight fit · encoder unloaded after use
56Good quality (B) · 56 of 100: how capable the model is at FP8, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking. DreamShaper XL v2 TurboLykon FP8 5.6 GB 18 s Runs welltight fit
39Basic quality (D) · 39 of 100: how capable the model is at FP8, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. SDXL TurboStability AI FP8 4.2 GB 2.1 s Runs great
60Good quality (B) · 60 of 100: how capable the model is at FP8, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking. Juggernaut XL v9RunDiffusion FP8 5.6 GB 64 s Slowtight fit

Best Video models for the NVIDIA Jetson Orin Nano Super (8 GB)

Quality Model Quant Memory Speed Runs
35Basic quality (D) · 35 of 100: how capable the model is at FP16, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking. AnimateDiff v3 (SD 1.5)Shanghai AI Lab FP16 4.7 GB 5.2 min Runs well
49Fair quality (C) · 49 of 100: how capable the model is at FP8, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking. LTX-Video 2BLightricks FP8 4.0 GB 7.7 min Runs wellCPU offload
44Fair quality (C) · 44 of 100: how capable the model is at FP8, whatever it runs on. Match score 52/100 (quality, fit, speed and memory use) sets the ranking. CogVideoX 2BZhipu AI FP8 4.7 GB 18.4 min SlowCPU offload

Best Decision models for the NVIDIA Jetson Orin Nano Super (8 GB)

Quality Model Quant Memory Speed Runs
80Great quality (A) · 80 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. GLiNER2.5 DecideFastino Q8_0 1.2 GB 94 ms Runs great
86Excellent quality (A+) · 86 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. Jina Reranker v3.5Jina AI Q8_0 2.0 GB 217 ms Runs well
76Great quality (A) · 76 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. Llama Prompt Guard 2 86MMeta Q8_0 1.0 GB 107 ms Runs great
74Great quality (A) · 74 of 100: how capable the model is at F16, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. GLiClass ModernBERT Large v3Knowledgator F16 1.5 GB 89 ms Runs great
78Great quality (A) · 78 of 100: how capable the model is at F16, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. OpenAI Privacy FilterOpenAI F16 3.8 GB 15 ms Runs great
80Great quality (A) · 80 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. FRIDA-DecisionsSber AI Q8_0 1.7 GB 155 ms Runs well
84Great quality (A) · 84 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 87/100 (quality, fit, speed and memory use) sets the ranking. Jina Reranker v3Jina AI Q8_0 2.0 GB 217 ms Runs well
72Great quality (A) · 72 of 100: how capable the model is at F16, whatever it runs on. Match score 87/100 (quality, fit, speed and memory use) sets the ranking. DeBERTa v3 Large Zero-shot v2.0Moritz Laurer F16 1.6 GB 97 ms Runs great

Best LLMs by task on this hardware Open in Model Finder

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.

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