The primary limiting factor in running DeepSeek-V3 (671B parameters) on an NVIDIA RTX 3070 is the substantial VRAM requirement. DeepSeek-V3, in FP16 precision, demands approximately 1342GB of VRAM to load the entire model. The RTX 3070, equipped with only 8GB of GDDR6 VRAM, falls far short of this requirement, resulting in a VRAM deficit of 1334GB. This discrepancy prevents the model from being loaded onto the GPU for inference. Even if aggressive quantization techniques are employed, the sheer size of the model poses a significant challenge for the 3070's memory capacity.
Furthermore, while the RTX 3070's memory bandwidth of 0.45 TB/s is respectable for its class, it becomes a bottleneck when dealing with models of this scale. Loading model weights and transferring data during inference would be significantly hampered, even if the model could somehow fit into the available VRAM. The 5888 CUDA cores and 184 Tensor cores, while capable, are ultimately limited by the memory constraints, rendering them largely ineffective for DeepSeek-V3. The Ampere architecture provides some performance advantages, but these are insufficient to overcome the fundamental VRAM limitation.
Due to the massive VRAM requirements of DeepSeek-V3, running it directly on an RTX 3070 is not feasible. Consider exploring cloud-based GPU instances with significantly higher VRAM, such as those offered by NelsaHost, which provide access to GPUs with 80GB of VRAM or more. Alternatively, investigate model parallelism techniques that split the model across multiple GPUs, although this requires specialized software and hardware configurations. If you are set on using your RTX 3070, explore smaller models or distilled versions of DeepSeek-V3 that have lower VRAM footprints. Fine-tuning a smaller, more manageable model for your specific task might be a more practical approach.
Another avenue to explore is offloading layers to system RAM, although this will drastically reduce inference speed. Quantization to INT4 or even lower precision might reduce the VRAM footprint, but it will likely still be insufficient to fit the entire model into 8GB of VRAM without significant performance degradation. Even with aggressive optimization, expect extremely slow inference speeds, potentially making it unusable for real-time applications.