The NVIDIA RTX 3080 Ti, with its 12GB of GDDR6X VRAM, falls significantly short of the 472GB required to load the DeepSeek-Coder-V2 model in FP16 precision. This massive discrepancy means the model cannot be directly loaded onto the GPU for inference. The RTX 3080 Ti's 10240 CUDA cores and 0.91 TB/s memory bandwidth are irrelevant in this scenario, as the primary bottleneck is the insufficient VRAM. Even with its Ampere architecture and 320 Tensor Cores, designed to accelerate AI workloads, the sheer size of the model prohibits its use on this GPU without substantial modifications.
Due to the extreme VRAM difference, running DeepSeek-Coder-V2 directly on the RTX 3080 Ti is not feasible without model quantization and offloading techniques. Consider using quantization methods like 4-bit or even lower to drastically reduce the model's memory footprint. Frameworks like `llama.cpp` are designed for CPU + GPU inference and can leverage system RAM to compensate for limited VRAM. Another option is to use a cloud-based inference service or a multi-GPU setup with aggregate VRAM exceeding 472GB, which is likely a more practical solution for most users.