The NVIDIA RTX 4070, with its 12GB of GDDR6X VRAM, falls significantly short of the memory requirements for running DeepSeek-V3. DeepSeek-V3, a 671B parameter model, necessitates approximately 1342GB of VRAM when using FP16 precision. This immense gap of 1330GB means the entire model cannot be loaded onto the RTX 4070 for inference. The RTX 4070's memory bandwidth of 0.5 TB/s, while respectable for its class, becomes irrelevant in this scenario as the model's sheer size prevents even partial loading for iterative processing.
Even with aggressive quantization techniques, such as 4-bit or 2-bit quantization, the memory footprint of DeepSeek-V3 remains far beyond the RTX 4070's capacity. Furthermore, the 5888 CUDA cores and 184 Tensor cores, while capable, are bottlenecked by the inability to load the model. Consequently, attempting to run DeepSeek-V3 directly on the RTX 4070 will result in out-of-memory errors. Performance metrics like tokens/sec and batch size are essentially undefined in this scenario because the model cannot be executed.
Running DeepSeek-V3 on a single RTX 4070 is not feasible due to the extreme VRAM requirements. Consider using cloud-based inference services that offer access to GPUs with sufficient memory, such as those found on vast.ai or similar platforms. Alternatively, explore model parallelism techniques across multiple GPUs, though this adds significant complexity and requires specialized software and expertise.
If you are committed to using the RTX 4070, focus on smaller, more manageable models that fit within its 12GB VRAM. There are numerous excellent open-source models with parameter counts in the billions, rather than hundreds of billions, that can be effectively run on this GPU. Fine-tuning a smaller model for a specific task might also be a viable alternative to achieve desired results without the immense resource demands of DeepSeek-V3.