The DeepSeek-Coder-V2 model, with its 236 billion parameters, presents a significant challenge for the NVIDIA Jetson Orin Nano 8GB due to its substantial VRAM requirement. In FP16 (half-precision floating point), DeepSeek-Coder-V2 demands approximately 472GB of VRAM. The Jetson Orin Nano 8GB, equipped with only 8GB of LPDDR5 memory, falls drastically short of this requirement, resulting in a VRAM deficit of 464GB. This incompatibility means the entire model cannot be loaded onto the GPU for inference, leading to a complete failure to run without significant modifications.
Beyond VRAM limitations, the memory bandwidth of the Jetson Orin Nano (70 GB/s) would also become a bottleneck even if the model could somehow fit into the available memory. Large language models like DeepSeek-Coder-V2 benefit from high memory bandwidth to quickly transfer weights and intermediate activations during the forward pass. The limited bandwidth would severely throttle the model's inference speed, making it impractical for real-time applications. The architecture of the Jetson Orin Nano, while based on Ampere and containing Tensor Cores, is designed for edge AI tasks and not the resource-intensive demands of a model of this scale.
Due to the extreme VRAM discrepancy, directly running DeepSeek-Coder-V2 on the NVIDIA Jetson Orin Nano 8GB is not feasible. Consider exploring smaller, more efficient models designed for edge deployment, such as distilled or quantized versions of similar code generation models. Alternatively, offloading some layers to the CPU (CPU offloading) or using techniques like model parallelism across multiple devices could be explored, but these approaches introduce significant complexity and performance overhead. Another option is to use cloud-based inference services where the model is hosted on more powerful hardware and accessed remotely via API calls.
If you are set on using a model of this scale, consider upgrading to a system with significantly more VRAM, such as a high-end NVIDIA RTX or A-series GPU with at least 48GB of VRAM. Furthermore, explore techniques like quantization (e.g., using 4-bit or 8-bit quantization) to reduce the model's memory footprint, although this may come at the cost of some accuracy. Finally, remember to choose the right inference framework.