Best AI Models for NVIDIA Jetson Orin Nano 8GB (8.0GB)
8.0 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?
Showing compatibility for NVIDIA Jetson Orin Nano 8GB
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
| Q8_0 | 4.9 GB61% | 9.0 t/s | 4K | GOOD FIT | A77 | |
| Q4_K_M | 2.9 GB36% | 15.3 t/s | 41K | FAIR FIT | B51 | |
| Q4_K_M | 0.7 GB8% | 67.0 t/s | 131K | EASY RUN | D29 | |
| Q4_K_M | 0.7 GB8% | 67.0 t/s | 33K | EASY RUN | D29 | |
| Q4_K_M | 2.6 GB33% | 16.7 t/s | 2K | FAIR FIT | B48 | |
| Q4_K_M | 2.9 GB36% | 15.5 t/s | 131K | FAIR FIT | B51 | |
| Q4_K_M | 1.0 GB13% | 43.8 t/s | 2K | EASY RUN | C32 | |
| Q4_K_M | 2.0 GB25% | 22.3 t/s | 131K | EASY RUN | C40 |
NVIDIA Jetson Orin Nano 8GB Specifications
- Brand
- NVIDIA
- Chip
- Orin Nano
- Type
- AI Box
- Unified Memory
- 8.0 GB
- Memory Bandwidth
- 68.0 GB/s
- GPU Cores
- 1024
- CPU Cores
- 6
- Neural Engine
- 67.0 TOPS
- Release Date
- 2023-03-08
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Frequently Asked Questions
- Can NVIDIA Jetson Orin Nano 8GB run Llama 3 8B?
Yes, the NVIDIA Jetson Orin Nano 8GB with 8 GB unified memory can run Llama 3 8B at multiple quantization levels. At Q4_K_M (the recommended starting point), you'll get smooth token generation suitable for interactive chat and coding assistance.
- 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 overhead (~3.5 GB), approximately 4.5 GB is available for AI models. This unified memory architecture is efficient since the GPU and CPU share the same memory pool without copy overhead.
- 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 LLM models. Apple Silicon's unified memory and Metal acceleration provide a smooth local AI experience.
- What's the best model for NVIDIA Jetson Orin Nano 8GB?
For the NVIDIA Jetson Orin Nano 8GB, we recommend starting with Phi 3 Mini at Q5_K_M for fast responses, or Llama 3 8B at Q4_K_M for a more capable assistant. Use Ollama or LM Studio for easy setup.
- How fast is NVIDIA Jetson Orin Nano 8GB for AI inference?
Token generation speed depends on the model and quantization. With 68.0 GB/s memory bandwidth, you can expect 15-35 tokens per second on 7B models at Q4_K_M, which is comfortable for real-time chat interaction.