Best AI Models for NVIDIA Jetson AGX Orin 64GB
64 GB unified − 3.5 GB OS overhead = 60.5 GB available for AI models
With 64 GB of memory, this is a high-end configuration for local AI. You can comfortably run most open-source LLMs including large 70B parameter models at good quantization levels, making it one of the best setups for serious local AI work.
At this memory tier, nearly every popular open-source model is within reach. You can run Llama 3 70B at Q4_K_M or even Q5_K_M quantization with room to spare, handle coding assistants like DeepSeek Coder 33B at high quality, and easily run any 7B–30B model at full or near-full precision. Context windows remain generous even with larger models, so multi-turn conversations and long-document processing work smoothly.
Runs Well
- 70B models (Llama 3 70B, Qwen 72B) at Q4–Q5
- 30B models at Q6–Q8 quality
- 7B–14B models at full FP16 precision
- Vision models (LLaVA, CogVLM) without compromise
Challenging
- Mixture-of-experts models like Mixtral 8x22B at higher quants
- 120B+ models still require lower quantizations
What LLMs Can NVIDIA Jetson AGX Orin 64GB Run?
32 models · 3 excellent
Showing compatibility for NVIDIA Jetson AGX Orin 64GB
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
Q4_K_M·2.9 t/s tok/s·131K ctx·GREAT FIT | Q4_K_M | 46.6 GB | 2.9 t/s | 131K | GREAT FIT | S88 |
Q4_K_M·2.9 t/s tok/s·131K ctx·GREAT FIT | Q4_K_M | 46.2 GB | 2.9 t/s | 131K | GREAT FIT | S88 |
Q4_K_M·3.0 t/s tok/s·33K ctx·GREAT FIT | Q4_K_M | 44.6 GB | 3.0 t/s | 33K | GREAT FIT | S86 |
Q4_K_M·4.7 t/s tok/s·33K ctx·FAIR FIT | Q4_K_M | 28.6 GB | 4.7 t/s | 33K | FAIR FIT | B60 |
Q4_K_M·67.2 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 2.0 GB | 67.2 t/s | 131K | EASY RUN | D27 |
Q4_K_M·46.1 t/s tok/s·41K ctx·EASY RUN | Q4_K_M | 2.9 GB | 46.1 t/s | 41K | EASY RUN | D28 |
Q4_K_M·201.7 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 0.7 GB | 201.7 t/s | 131K | EASY RUN | D26 |
Q4_K_M·50.4 t/s tok/s·2K ctx·EASY RUN | Q4_K_M | 2.6 GB | 50.4 t/s | 2K | EASY RUN | D27 |
Q4_K_M·201.7 t/s tok/s·33K ctx·EASY RUN | Q4_K_M | 0.7 GB | 201.7 t/s | 33K | EASY RUN | D26 |
Q4_K_M·131.8 t/s tok/s·2K ctx·EASY RUN | Q4_K_M | 1.0 GB | 131.8 t/s | 2K | EASY RUN | D26 |
Q4_K_M·6.7 t/s tok/s·41K ctx·FAIR FIT | Q4_K_M | 19.8 GB | 6.7 t/s | 41K | FAIR FIT | B46 |
Q4_K_M·100.8 t/s tok/s·8K ctx·EASY RUN | Q4_K_M | 1.3 GB | 100.8 t/s | 8K | EASY RUN | D26 |
Q4_K_M·46.7 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 2.9 GB | 46.7 t/s | 131K | EASY RUN | D27 |
Q4_K_M·6.5 t/s tok/s·131K ctx·FAIR FIT | Q4_K_M | 20.5 GB | 6.5 t/s | 131K | FAIR FIT | B47 |
Q4_K_M·6.5 t/s tok/s·33K ctx·FAIR FIT | Q4_K_M | 20.5 GB | 6.5 t/s | 33K | FAIR FIT | B47 |
Q4_K_M·26.7 t/s tok/s·33K ctx·EASY RUN | Q4_K_M | 5.0 GB | 26.7 t/s | 33K | EASY RUN | D29 |
NVIDIA Jetson AGX Orin 64GB Specifications
- Brand
- NVIDIA
- Chip
- Orin
- Type
- AI Box
- Unified Memory
- 64 GB
- Memory Bandwidth
- 204.8 GB/s
- GPU Cores
- 2048
- CPU Cores
- 12
- Neural Engine
- 275.0 TOPS
- Release Date
- 2022-03-22
Get Started
Devices to Consider
Similar devices and upgrades with more memory or higher bandwidth
Frequently Asked Questions
- Can NVIDIA Jetson AGX Orin 64GB run Llama 3.1 70B Instruct?
Yes, the NVIDIA Jetson AGX Orin 64GB with 64 GB unified memory can run Llama 3.1 70B Instruct, Llama 3.3 70B Instruct, Qwen2.5 72B Instruct, and 1253 other models. 43 models achieve excellent performance, and 30 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 AGX Orin 64GB?
The NVIDIA Jetson AGX Orin 64GB has 64 GB unified memory. After macOS reserves ~3.5 GB for the operating system, approximately 60.5 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 AGX Orin 64GB good for AI?
With 64 GB unified memory and 204.8 GB/s bandwidth, the NVIDIA Jetson AGX Orin 64GB is excellent for running local AI models. It supports 73 models at good quality or better. This is a premium configuration — you can run large 30B+ parameter models at good quality, and most 7B models at maximum quality. Ideal for professional AI workloads.
- What's the best model for NVIDIA Jetson AGX Orin 64GB?
The top-rated models for the NVIDIA Jetson AGX Orin 64GB are Llama 3.1 70B Instruct, Llama 3.3 70B Instruct, Qwen2.5 72B Instruct. With this much memory, you can prioritize quality — use higher quantizations (Q5/Q6) for better output, or run larger 30B+ models for more capable reasoning.
- How fast is NVIDIA Jetson AGX Orin 64GB for AI inference?
With 204.8 GB/s memory bandwidth, the NVIDIA Jetson AGX Orin 64GB achieves approximately 32 tok/s on a 7B model at Q4_K_M — that's comfortable for real-time chat. A 14B model runs at ~16 tok/s. Apple Silicon achieves high efficiency (~70%) thanks to unified memory — there's no PCIe bottleneck between CPU and GPU.
tok/s = (204.8 GB/s ÷ model GB) × efficiency
Apple Silicon achieves ~70% bandwidth efficiency thanks to unified memory and Metal acceleration.
Estimated speed on NVIDIA Jetson AGX Orin 64GB
~3 tok/s~3 tok/s~3 tok/s~5 tok/sReal-world results typically within ±20%.
- Can I run AI offline on NVIDIA Jetson AGX Orin 64GB?
Yes — once you download a model, it runs entirely on the NVIDIA Jetson AGX Orin 64GB 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.