The NVIDIA Jetson Orin Nano 8GB is fundamentally incompatible with running DeepSeek-V2.5 due to a massive VRAM deficit. DeepSeek-V2.5, with its 236 billion parameters, requires approximately 472GB of VRAM when using FP16 precision. The Orin Nano provides only 8GB of VRAM. This 464GB shortfall means the entire model cannot be loaded onto the GPU at once. Furthermore, even if techniques like offloading layers to system RAM were employed, the relatively low memory bandwidth of 70 GB/s on the Orin Nano would create a severe bottleneck, resulting in extremely slow inference speeds, likely making it unusable in practice. The Ampere architecture of the Orin Nano, while supporting tensor cores, cannot compensate for the sheer scale difference in memory requirements.
Running DeepSeek-V2.5 directly on the Jetson Orin Nano 8GB is not feasible. Consider exploring smaller language models specifically designed for edge devices with limited resources, such as those with parameter counts in the millions rather than billions. Alternatively, offload inference to a more powerful cloud-based GPU server. Fine-tuning a smaller, more manageable model on a dataset relevant to your use case might also provide a better experience on the Orin Nano. For local execution, investigate optimized models specifically built for the Jetson platform.