The NVIDIA RTX 3070 Ti, with its 8GB of GDDR6X VRAM, falls significantly short of the 472GB required to load the DeepSeek-Coder-V2 model in FP16 precision. This massive discrepancy means the entire model cannot reside on the GPU's memory, leading to inevitable out-of-memory errors if a direct attempt is made to load the model without any modifications. The RTX 3070 Ti's memory bandwidth of 0.61 TB/s, while respectable, is also a limiting factor even if VRAM capacity were sufficient, as swapping data between system RAM and GPU memory would introduce severe performance bottlenecks.
Even with techniques like CPU offloading, the performance would be severely hampered due to the relatively slow transfer speeds between the system RAM and the GPU. The 6144 CUDA cores and 192 Tensor cores on the RTX 3070 Ti would be underutilized, as the primary bottleneck becomes memory capacity rather than computational power. Therefore, running DeepSeek-Coder-V2 on an RTX 3070 Ti without substantial modifications is practically infeasible.
Given the severe VRAM limitations, directly running DeepSeek-Coder-V2 on the RTX 3070 Ti is not recommended. Consider exploring extreme quantization techniques like 4-bit or even 3-bit quantization using libraries like `llama.cpp` or `AutoGPTQ`. These techniques drastically reduce the model's memory footprint, potentially making it fit within the 8GB VRAM. However, expect a significant reduction in model accuracy and generation quality.
Alternatively, consider using cloud-based inference services that offer GPUs with sufficient VRAM to run the model or leveraging distributed inference across multiple GPUs if feasible. Another approach is to use a smaller, more efficient model that is specifically designed to run on lower-resource hardware. Fine-tuning a smaller model for code generation tasks might provide a more practical solution for your hardware.