Best AI Models for NVIDIA Jetson Orin NX 16GB
16 GB total — ~13 GB usable as VRAM
Embedded edge module (16 GB, 102 GB/s) — sized for robotics/IoT; small-to-mid models only.
16 GB is a comfortable mid-range tier for local AI. Most 7B–13B models run smoothly at good quantization levels, and smaller models can run at near-full precision.
This memory tier strikes a nice balance between price and capability. Popular 7B models like Llama 3 8B, Mistral 7B, and Qwen 2.5 7B all run very well at Q4_K_M quantization with fast inference and reasonable context windows. You can also fit some larger 13B models at Q3–Q4, though you'll want to keep context lengths modest. Small models like Phi 3 Mini (3.8B) practically fly at Q8 or even FP16 quality.
Runs Well
- 7B models at Q4–Q6 quality with good speed
- Small models (3B–4B) at Q8 or FP16
- 9B models (Gemma 2 9B) at Q4_K_M
Challenging
- 13B–14B models need Q3 or lower
- 30B+ models do not fit in VRAM
- Long context (>8K tokens) with larger models
What LLMs Can NVIDIA Jetson Orin NX 16GB Run?
83 models · 4 excellent · 13 good
Showing compatibility for NVIDIA Jetson Orin NX 16GB
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
Q4_K_M·7.0 t/s tok/s·16K ctx·GREAT FIT | Q4_K_M | 9.5 GB | 7.0 t/s | 16K | GREAT FIT | S88 |
Q4_K_M·7.0 t/s tok/s·33K ctx·GREAT FIT | Q4_K_M | 9.5 GB | 7.0 t/s | 33K | GREAT FIT | S88 |
Q4_K_M·8.1 t/s tok/s·262K ctx·GOOD FIT | Q4_K_M | 8.2 GB | 8.1 t/s | 262K | GOOD FIT | A80 |
Q4_K_M·8.3 t/s tok/s·33K ctx·GOOD FIT | Q4_K_M | 8.0 GB | 8.3 t/s | 33K | GOOD FIT | A78 |
Q4_K_M·7.7 t/s tok/s·GOOD FIT | Q4_K_M | 8.6 GB | 7.7 t/s | — | GOOD FIT | A83 |
Q4_K_M·7.1 t/s tok/s·8K ctx·GREAT FIT | Q4_K_M | 9.3 GB | 7.1 t/s | 8K | GREAT FIT | S88 |
Q4_K_M·7.1 t/s tok/s·8K ctx·GREAT FIT | Q4_K_M | 9.3 GB | 7.1 t/s | 8K | GREAT FIT | S88 |
Q4_K_M·7.7 t/s tok/s·GOOD FIT | Q4_K_M | 8.6 GB | 7.7 t/s | — | GOOD FIT | A83 |
Q4_K_M·8.2 t/s tok/s·131K ctx·GOOD FIT | Q4_K_M | 8.1 GB | 8.2 t/s | 131K | GOOD FIT | A78 |
Q4_K_M·7.8 t/s tok/s·GOOD FIT | Q4_K_M | 8.6 GB | 7.8 t/s | — | GOOD FIT | A83 |
Q4_K_M·7.8 t/s tok/s·GOOD FIT | Q4_K_M | 8.6 GB | 7.8 t/s | — | GOOD FIT | A83 |
Q4_K_M·7.8 t/s tok/s·2K ctx·GOOD FIT | Q4_K_M | 8.6 GB | 7.8 t/s | 2K | GOOD FIT | A83 |
Q4_K_M·6.4 t/s tok/s·33K ctx·GOOD FIT | Q4_K_M | 10.5 GB | 6.4 t/s | 33K | GOOD FIT | A81 |
Q4_K_M·7.8 t/s tok/s·GOOD FIT | Q4_K_M | 8.6 GB | 7.8 t/s | — | GOOD FIT | A83 |
Q4_K_M·9.5 t/s tok/s·GOOD FIT | Q4_K_M | 7.0 GB | 9.5 t/s | — | GOOD FIT | A69 |
Q4_K_M·9.1 t/s tok/s·8K ctx·GOOD FIT | Q4_K_M | 7.3 GB | 9.1 t/s | 8K | GOOD FIT | A71 |
NVIDIA Jetson Orin NX 16GB Specifications
- Brand
- NVIDIA
- Chip
- Orin NX
- Type
- AI Box
- Unified Memory
- 16 GB
- Memory Bandwidth
- 102.4 GB/s
- GPU Cores
- 1024
- CPU Cores
- 8
- Neural Engine
- 157.0 TOPS
- Architecture
- Ampere
- Memory Type
- LPDDR5
- TDP
- 10-40 W
- MSRP
- $599
- Release Date
- 2023-02-01
Get Started
Devices to Consider
Similar devices and upgrades with more memory or higher bandwidth
Frequently Asked Questions
- Can NVIDIA Jetson Orin NX 16GB run Phi 4?
Yes, the NVIDIA Jetson Orin NX 16GB with 16 GB unified memory can run Phi 4, Phi 4 Reasoning, Gemma 4 12B IT, and 1075 other models. 69 models achieve excellent performance, and 132 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 NX 16GB?
The NVIDIA Jetson Orin NX 16GB has 16 GB unified memory. After macOS reserves ~3.5 GB for the operating system, approximately 12.5 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 NX 16GB good for AI?
With 16 GB unified memory and 102.4 GB/s bandwidth, the NVIDIA Jetson Orin NX 16GB is good for running local AI models. It supports 201 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 NX 16GB?
The top-rated models for the NVIDIA Jetson Orin NX 16GB are Phi 4, Phi 4 Reasoning, Gemma 4 12B IT. 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 NX 16GB for AI inference?
With 102.4 GB/s memory bandwidth, the NVIDIA Jetson Orin NX 16GB achieves approximately 16 tok/s on a 7B model at Q4_K_M — that's functional for interactive use. A 14B model runs at ~8 tok/s. Apple Silicon achieves high efficiency (~70%) thanks to unified memory — there's no PCIe bottleneck between CPU and GPU.
tok/s = (102.4 GB/s ÷ model GB) × efficiency
Apple Silicon achieves ~70% bandwidth efficiency thanks to unified memory and Metal acceleration.
Estimated speed on NVIDIA Jetson Orin NX 16GB
~7 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 NX 16GB?
Yes — once you download a model, it runs entirely on the NVIDIA Jetson Orin NX 16GB 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.
- Anything to watch out for with NVIDIA Jetson Orin NX 16GB?
Embedded edge module (16 GB, 102 GB/s) — sized for robotics/IoT; small-to-mid models only.