The primary limiting factor in running large language models (LLMs) like DeepSeek-V3 is VRAM (Video RAM). DeepSeek-V3, with its 671 billion parameters, requires approximately 1342 GB of VRAM when using FP16 (half-precision floating point) for storing the model weights. The NVIDIA RTX 4080, while a powerful gaming and workstation GPU, only offers 16GB of VRAM. This creates a massive VRAM deficit of 1326 GB, making it impossible to load the entire model into the GPU's memory for inference at FP16 precision. Memory bandwidth, while important for performance, becomes secondary when the model cannot even fit into the available VRAM. The Ada Lovelace architecture of the RTX 4080 provides excellent compute capabilities, but these cannot be utilized effectively without sufficient memory to hold the model.
Unfortunately, running DeepSeek-V3 with its full 671 billion parameters on an RTX 4080 is not feasible due to the VRAM limitations. You'll need to explore alternative approaches such as using a smaller model, quantizing the model to a lower precision (e.g., 4-bit or 8-bit), offloading layers to system RAM (which will significantly reduce performance), or distributing the model across multiple GPUs. Consider using cloud-based GPU services that offer instances with sufficient VRAM if you need to work with the full DeepSeek-V3 model. Another potential option is to use a distilled or fine-tuned version of DeepSeek-V3 that has fewer parameters and thus lower VRAM requirements.