The NVIDIA RTX 4070 Ti SUPER, while a powerful card, falls short of the immense VRAM requirements of the DeepSeek-V3 model. DeepSeek-V3, with its 671 billion parameters, necessitates a staggering 1342GB of VRAM when running in FP16 (half-precision floating point). The RTX 4070 Ti SUPER only offers 16GB of GDDR6X VRAM. This creates a massive VRAM deficit of 1326GB, making direct inference impossible without significant modifications. The memory bandwidth of 0.67 TB/s on the RTX 4070 Ti SUPER, while respectable, is secondary to the VRAM bottleneck in this scenario. Even if the data could be transferred quickly, the card lacks the capacity to hold the model in memory.
Directly running DeepSeek-V3 on an RTX 4070 Ti SUPER is not feasible due to the extreme VRAM disparity. To work around this, consider model quantization techniques like 4-bit or even 2-bit quantization to significantly reduce the model's memory footprint. Frameworks like `llama.cpp` or `text-generation-inference` are crucial for implementing these optimizations. Alternatively, explore cloud-based inference solutions or distributed computing across multiple GPUs with sufficient VRAM if high performance is critical and quantization is not sufficient. Fine-tuning a smaller, more manageable model that approximates DeepSeek-V3's capabilities could also be a viable strategy for local deployment.