The DeepSeek-V3 model, with its 671 billion parameters, presents a significant challenge for the NVIDIA RTX 3080 12GB. The primary bottleneck lies in VRAM. DeepSeek-V3, when running in FP16 (half-precision floating point), requires approximately 1342GB of VRAM to load the entire model. The RTX 3080 12GB only provides 12GB of VRAM, resulting in a massive shortfall of 1330GB. This discrepancy means the model cannot be loaded and run directly on the GPU without significant modifications. While the RTX 3080's memory bandwidth of 0.91 TB/s is substantial, it becomes irrelevant when the model cannot even fit within the available memory. Similarly, the CUDA and Tensor cores, while powerful, cannot compensate for the fundamental lack of memory capacity. The Ampere architecture is capable, but limited by the constraints of the available VRAM. The 350W TDP is also not a limiting factor in this scenario.
Given the vast VRAM difference, running DeepSeek-V3 directly on the RTX 3080 12GB is not feasible. To experiment with this model, consider offloading layers to system RAM, which will drastically reduce inference speed. Alternatively, explore quantization techniques such as Q4 or even lower precisions using libraries like `llama.cpp` to reduce the model's memory footprint. Cloud-based solutions with access to higher VRAM GPUs (e.g., NVIDIA A100, H100) or distributed inference across multiple GPUs are other viable options. Fine-tuning a smaller, more manageable model might also be a more practical approach for local experimentation with the RTX 3080 12GB.