The DeepSeek-V2.5 model, with its massive 236 billion parameters, presents a significant challenge for the NVIDIA RTX 3080 10GB. Running this model in FP16 (half-precision floating point) requires approximately 472GB of VRAM. The RTX 3080, equipped with only 10GB of GDDR6X memory, falls drastically short, lacking 462GB of the necessary VRAM. This enormous deficit means the entire model cannot be loaded onto the GPU at once, preventing direct inference. The RTX 3080's memory bandwidth of 0.76 TB/s, while substantial, becomes irrelevant in this scenario because the primary bottleneck is insufficient VRAM, not data transfer speed.
Directly running DeepSeek-V2.5 on the RTX 3080 10GB is not feasible due to the extreme VRAM requirements. To make any progress, you'll need to explore aggressive quantization techniques such as 4-bit or even lower precision. Consider using inference frameworks like `llama.cpp` which are optimized for CPU+GPU inference and can offload layers to system RAM. However, be prepared for significantly reduced inference speed as the model will be heavily reliant on system memory. Alternatively, explore cloud-based inference services or consider upgrading to a GPU with substantially more VRAM (48GB or more) if local execution is a must.