The DeepSeek-V3 model, with its 671 billion parameters, presents a significant challenge for the NVIDIA Jetson AGX Orin 32GB. The model's FP16 (half-precision floating point) VRAM requirement is approximately 1342GB. This vastly exceeds the Jetson AGX Orin's 32GB of LPDDR5 VRAM, resulting in a massive VRAM deficit of 1310GB. This discrepancy means the entire model cannot be loaded onto the GPU for inference. Furthermore, even if aggressive quantization techniques were applied, the sheer size of the model makes fitting it into the available memory highly improbable. The Jetson AGX Orin's memory bandwidth of 0.21 TB/s, while decent for its class, would also likely become a bottleneck if the model could somehow be squeezed into memory, resulting in severely degraded performance.
Due to the extreme VRAM requirements of DeepSeek-V3, direct inference on the NVIDIA Jetson AGX Orin 32GB is not feasible. Instead, consider exploring alternative, smaller models that are specifically designed for edge devices with limited resources. Options include quantized versions of smaller LLMs, or models fine-tuned for specific tasks that require less computational power. Alternatively, you can explore offloading inference to a more powerful cloud-based GPU or a local server equipped with a high-VRAM GPU. For local execution, consider a desktop GPU with 48GB+ VRAM like an RTX 3090 or RTX 4090 coupled with aggressive quantization techniques.