The NVIDIA RTX 4070 Ti, with its 12GB of GDDR6X VRAM, falls significantly short of the memory requirements for running DeepSeek-V3, a 671 billion parameter model. DeepSeek-V3 in FP16 precision demands approximately 1342GB of VRAM. The 4070 Ti's memory bandwidth of 0.5 TB/s, while respectable, is insufficient to handle the massive data throughput required by such a large model even if it could fit in memory. The substantial VRAM deficit means the model cannot be loaded onto the GPU for inference without employing aggressive quantization techniques or distributed inference across multiple GPUs. Attempting to run DeepSeek-V3 on the 4070 Ti without significant optimization will result in an out-of-memory error.
Due to the extreme VRAM discrepancy, directly running DeepSeek-V3 on a single RTX 4070 Ti is impractical. Consider using extreme quantization techniques, such as 4-bit or even 2-bit quantization, to reduce the model's memory footprint. Frameworks like `llama.cpp` or `text-generation-inference` are essential for leveraging these quantization methods. Alternatively, explore distributed inference solutions that split the model across multiple GPUs, or utilize cloud-based inference services that offer the necessary resources. If local inference is a must, consider smaller models or models specifically designed for lower VRAM configurations.