The DeepSeek-V3 model, with its 671 billion parameters, presents a significant challenge for consumer-grade GPUs like the NVIDIA RTX 3060 12GB. DeepSeek-V3 in FP16 (half-precision floating point) requires approximately 1342GB of VRAM to load the entire model. The RTX 3060, equipped with only 12GB of VRAM, falls drastically short of this requirement. This immense discrepancy means the model cannot be loaded and run directly on the GPU without substantial modifications.
Beyond VRAM, memory bandwidth also plays a crucial role in inference speed. The RTX 3060's memory bandwidth of 0.36 TB/s, while adequate for many tasks, would become a bottleneck even if the model *could* fit into VRAM. The constant swapping of model layers between system RAM and the GPU would drastically reduce the tokens/second generated. This is because large language models require frequent memory access, and limited bandwidth severely restricts the rate at which data can be transferred, leading to performance degradation. CUDA cores and tensor cores, while important for computation, are secondary concerns when VRAM is the primary limiting factor.
Given the enormous VRAM disparity, directly running DeepSeek-V3 on an RTX 3060 12GB is not feasible without significant compromises. Model quantization is essential; consider aggressively quantizing the model to 4-bit or even lower using techniques like bitsandbytes or llama.cpp's quantization methods. This will reduce the VRAM footprint, but will come at a cost of accuracy.
Alternatively, explore offloading layers to system RAM. Frameworks like llama.cpp allow you to specify the number of layers to keep on the GPU, offloading the rest to system RAM. However, this approach will severely impact inference speed. Another option is to use cloud-based inference services or distributed computing setups that can handle the model's memory demands. Finally, consider using a smaller model that fits within the RTX 3060's VRAM.