The DeepSeek-V3 model, with its 671 billion parameters, presents a significant challenge for the NVIDIA RTX 3080 10GB. At FP16 precision, the model requires approximately 1342GB of VRAM to load the entire model. The RTX 3080, equipped with only 10GB of VRAM, falls drastically short of this requirement. This immense VRAM disparity means the model cannot be loaded and run directly on the GPU without employing significant optimization techniques. Furthermore, even if aggressive quantization or offloading strategies are used, the limited memory bandwidth of 0.76 TB/s on the RTX 3080 will likely become a bottleneck, severely impacting inference speed.
Given the substantial VRAM deficit, directly running DeepSeek-V3 on the RTX 3080 10GB is impractical without significant compromises. Consider using extreme quantization techniques such as 4-bit or even 2-bit quantization to drastically reduce the VRAM footprint. Model offloading to system RAM is another option, but this will introduce significant performance penalties due to the slower transfer speeds between GPU and system memory. Alternatively, explore cloud-based inference services or consider upgrading to a GPU with significantly more VRAM, such as an NVIDIA RTX 4090 or an NVIDIA A100, if feasible.