The DeepSeek-V3 model, with its 671 billion parameters, presents a significant challenge for the NVIDIA RTX 5000 Ada due to its substantial VRAM requirements. Running DeepSeek-V3 in FP16 (half-precision floating point) mode demands approximately 1342GB of VRAM. The RTX 5000 Ada, equipped with only 32GB of GDDR6 memory, falls drastically short of this requirement, resulting in a VRAM deficit of 1310GB. This discrepancy makes it impossible to load the entire model into the GPU memory for inference. The memory bandwidth of 0.58 TB/s, while respectable, becomes irrelevant when the model cannot even reside in the available memory. CUDA and Tensor core counts are also inconsequential in this scenario, as they cannot be utilized without the model being loaded.
Directly running DeepSeek-V3 on the RTX 5000 Ada is not feasible due to the immense VRAM requirements. To potentially work around this, consider extreme quantization techniques like Q2 or even lower, which would significantly reduce the model's memory footprint. However, expect a considerable reduction in model accuracy. Alternatively, explore offloading layers to system RAM, although this will severely impact inference speed. A more practical approach would be to leverage cloud-based GPU instances with sufficient VRAM or explore distributed inference across multiple GPUs. Fine-tuning a smaller, more manageable model for your specific task might also yield better results on your current hardware.