Best AI Models for NVIDIA Jetson Orin Nano 8GB
8 GB unified − 3.5 GB OS overhead = 4.5 GB available for AI models
8 GB is an entry-level tier for local AI. You can run small 7B models at lower quantization levels, which is great for experimenting but comes with quality and speed trade-offs.
With 8 GB, you're limited to smaller models and lower quantization levels, but it's still enough for a meaningful local AI experience. Phi 3 Mini (3.8B) and similar compact models run well at Q4_K_M. For 7B models like Mistral 7B and Llama 3 8B, you'll need Q2_K or Q3_K_M quantization, which reduces output quality. Think of this tier as ideal for learning and experimentation rather than production workloads.
Runs Well
- 3B–4B models at Q4–Q5 quality
- 7B models at Q2–Q3 (usable but reduced quality)
- Quick experiments and learning
Challenging
- 7B models at Q4+ (VRAM too tight)
- Any model above 7B parameters
- Long context windows even with small models
What LLMs Can NVIDIA Jetson Orin Nano 8GB Run?
18 models · 2 excellent · 7 good
Showing compatibility for NVIDIA Jetson Orin Nano 8GB
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
Q4_K_M·8.0 t/s tok/s·41K ctx·GREAT FIT | Q4_K_M | 5.5 GB | 8.0 t/s | 41K | GREAT FIT | S85 |
Q4_K_M·7.2 t/s tok/s·8K ctx·GREAT FIT | Q4_K_M | 6.1 GB | 7.2 t/s | 8K | GREAT FIT | S89 |
Q4_K_M·8.4 t/s tok/s·131K ctx·GOOD FIT | Q4_K_M | 5.3 GB | 8.4 t/s | 131K | GOOD FIT | A83 |
Q4_K_M·8.2 t/s tok/s·131K ctx·GOOD FIT | Q4_K_M | 5.4 GB | 8.2 t/s | 131K | GOOD FIT | A84 |
Q4_K_M·8.9 t/s tok/s·33K ctx·GOOD FIT | Q4_K_M | 5.0 GB | 8.9 t/s | 33K | GOOD FIT | A78 |
Q4_K_M·8.2 t/s tok/s·131K ctx·GOOD FIT | Q4_K_M | 5.4 GB | 8.2 t/s | 131K | GOOD FIT | A84 |
Q4_K_M·9.0 t/s tok/s·33K ctx·GOOD FIT | Q4_K_M | 4.9 GB | 9.0 t/s | 33K | GOOD FIT | A78 |
Q4_K_M·8.9 t/s tok/s·131K ctx·GOOD FIT | Q4_K_M | 5.0 GB | 8.9 t/s | 131K | GOOD FIT | A78 |
Q8_0·9.0 t/s tok/s·4K ctx·GOOD FIT | Q8_0 | 4.9 GB | 9.0 t/s | 4K | GOOD FIT | A77 |
Q4_K_M·15.3 t/s tok/s·41K ctx·FAIR FIT | Q4_K_M | 2.9 GB | 15.3 t/s | 41K | FAIR FIT | B51 |
Q4_K_M·67.0 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 0.7 GB | 67.0 t/s | 131K | EASY RUN | D29 |
Q4_K_M·67.0 t/s tok/s·33K ctx·EASY RUN | Q4_K_M | 0.7 GB | 67.0 t/s | 33K | EASY RUN | D29 |
Q4_K_M·16.7 t/s tok/s·2K ctx·FAIR FIT | Q4_K_M | 2.6 GB | 16.7 t/s | 2K | FAIR FIT | B48 |
Q4_K_M·15.5 t/s tok/s·131K ctx·FAIR FIT | Q4_K_M | 2.9 GB | 15.5 t/s | 131K | FAIR FIT | B51 |
Q4_K_M·43.8 t/s tok/s·2K ctx·EASY RUN | Q4_K_M | 1.0 GB | 43.8 t/s | 2K | EASY RUN | C32 |
Q4_K_M·22.3 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 2.0 GB | 22.3 t/s | 131K | EASY RUN | C40 |
NVIDIA Jetson Orin Nano 8GB Specifications
- Brand
- NVIDIA
- Chip
- Orin Nano
- Type
- AI Box
- Unified Memory
- 8 GB
- Memory Bandwidth
- 68.0 GB/s
- GPU Cores
- 1024
- CPU Cores
- 6
- Neural Engine
- 67.0 TOPS
- Release Date
- 2023-03-08
Get Started
Devices to Consider
Similar devices and upgrades with more memory or higher bandwidth
Frequently Asked Questions
- Can NVIDIA Jetson Orin Nano 8GB run Qwen3 8B?
Yes, the NVIDIA Jetson Orin Nano 8GB with 8 GB unified memory can run Qwen3 8B, Gemma 2 9B IT, Llama 3.1 8B Instruct, and 666 other models. 55 models achieve excellent performance, and 197 run at good quality. Apple Silicon's unified memory architecture lets the GPU access the full memory pool without copying data, making it efficient for AI workloads.
- How much memory is available for AI on NVIDIA Jetson Orin Nano 8GB?
The NVIDIA Jetson Orin Nano 8GB has 8 GB unified memory. After macOS reserves ~3.5 GB for the operating system, approximately 4.5 GB is available for AI models. Unlike discrete GPUs where VRAM is separate from system RAM, Apple Silicon shares one memory pool between the CPU and GPU — this means no data copying overhead, but you share memory with macOS and open apps.
- Is NVIDIA Jetson Orin Nano 8GB good for AI?
With 8 GB unified memory and 68.0 GB/s bandwidth, the NVIDIA Jetson Orin Nano 8GB is good for running local AI models. It supports 252 models at good quality or better. It's a capable entry point for 7B models. Apple Silicon's Metal acceleration and unified memory make it surprisingly efficient despite the modest memory.
- What's the best model for NVIDIA Jetson Orin Nano 8GB?
The top-rated models for the NVIDIA Jetson Orin Nano 8GB are Qwen3 8B, Gemma 2 9B IT, Llama 3.1 8B Instruct. At this memory level, 7B models at Q4_K_M give you the best experience — fast responses and solid quality for chat and coding assistance.
- How fast is NVIDIA Jetson Orin Nano 8GB for AI inference?
With 68.0 GB/s memory bandwidth, the NVIDIA Jetson Orin Nano 8GB achieves approximately 11 tok/s on a 7B model at Q4_K_M — that's functional for interactive use. Apple Silicon achieves high efficiency (~70%) thanks to unified memory — there's no PCIe bottleneck between CPU and GPU.
tok/s = (68 GB/s ÷ model GB) × efficiency
Apple Silicon achieves ~70% bandwidth efficiency thanks to unified memory and Metal acceleration.
Estimated speed on NVIDIA Jetson Orin Nano 8GB
~8 tok/s~7 tok/s~8 tok/s~8 tok/sReal-world results typically within ±20%.
- Can I run AI offline on NVIDIA Jetson Orin Nano 8GB?
Yes — once you download a model, it runs entirely on the NVIDIA Jetson Orin Nano 8GB without internet. Applications like Ollama and LM Studio make it straightforward to download, manage, and run models locally. All your conversations stay private on your device with zero data sent to external servers. This is one of the key advantages of local AI: complete privacy, no API costs, and no rate limits.