The NVIDIA RTX 3090, equipped with 24GB of GDDR6X VRAM, falls significantly short of the 1342GB VRAM required to load the full DeepSeek-V3 (671B parameter) model in FP16 precision. This massive discrepancy means the model cannot be directly loaded onto the RTX 3090 for inference. The RTX 3090's memory bandwidth of 0.94 TB/s, while substantial, is also a limiting factor. Even if VRAM were sufficient, the high parameter count of DeepSeek-V3 would necessitate frequent memory access, potentially bottlenecking performance. Furthermore, the RTX 3090's 10496 CUDA cores and 328 Tensor cores would be heavily utilized, but the sheer size of the model would still result in slow processing speeds without significant optimization.
Due to the extreme VRAM requirements, running DeepSeek-V3 on a single RTX 3090 is practically infeasible without substantial model quantization or offloading strategies. Consider using extreme quantization techniques like 4-bit or even 3-bit quantization to drastically reduce the model's memory footprint. Alternatively, explore model parallelism across multiple GPUs or CPU offloading, though these methods introduce complexity and performance overhead. If possible, consider using cloud-based inference services or hardware with significantly more VRAM, such as the NVIDIA H100 or A100, to achieve reasonable performance with DeepSeek-V3.