The NVIDIA RTX 4080 SUPER, while a powerful card with 16GB of GDDR6X VRAM and a memory bandwidth of 0.74 TB/s, falls short when attempting to run DeepSeek-Coder-V2. DeepSeek-Coder-V2, with its 236 billion parameters, necessitates a staggering 472GB of VRAM in FP16 precision. This immense VRAM requirement stems from the need to store the model's weights and intermediate activations during inference. The RTX 4080 SUPER's 16GB VRAM is insufficient, resulting in a VRAM deficit of 456GB. This discrepancy prevents the model from loading entirely onto the GPU, leading to a compatibility failure and precluding any meaningful inference.
Given the significant VRAM disparity, directly running DeepSeek-Coder-V2 on a single RTX 4080 SUPER is not feasible. To work around this limitation, consider exploring model quantization techniques such as Q4 or even lower precisions, which drastically reduce the VRAM footprint. Alternatively, investigate distributed inference solutions, such as splitting the model across multiple GPUs or utilizing cloud-based inference services that offer the necessary VRAM capacity. Another option is to use a smaller model that fits within the 4080 SUPER's memory constraints. If high precision is not crucial, consider leveraging CPU offloading in conjunction with quantization, but be aware this will significantly impact performance.