The primary limiting factor in running large language models (LLMs) like DeepSeek-V2.5 on consumer GPUs is VRAM. DeepSeek-V2.5, with its 236 billion parameters, requires approximately 472GB of VRAM when using FP16 (half-precision floating point) for storing the model weights. The NVIDIA RTX 4080, equipped with 16GB of GDDR6X VRAM, falls drastically short of this requirement. This means the entire model cannot be loaded onto the GPU at once. Attempting to run the model without addressing this VRAM shortfall will result in out-of-memory errors. While techniques like offloading layers to system RAM exist, they severely impact performance due to the slower transfer speeds between system RAM and the GPU compared to the GPU's dedicated VRAM. The RTX 4080's memory bandwidth of 0.72 TB/s, while substantial, is still not sufficient to compensate for the massive data transfer overhead introduced by offloading.
Given the significant VRAM discrepancy, directly running DeepSeek-V2.5 on a single RTX 4080 is not feasible. Consider using aggressive quantization techniques, such as Q4 or even lower precisions, using llama.cpp or similar frameworks. Quantization reduces the memory footprint of the model, potentially allowing it to fit within the 16GB VRAM. Alternatively, explore cloud-based inference services or renting a more powerful GPU with sufficient VRAM, such as an NVIDIA A100 or H100. Model parallelism across multiple GPUs could also be an option, but it requires significant technical expertise and infrastructure. If possible, try running smaller models or fine-tuning a smaller model for your specific task.