The NVIDIA RTX 4070 Ti, equipped with 12GB of GDDR6X VRAM, falls significantly short of the VRAM requirements for the DeepSeek-Coder-V2 model, which necessitates approximately 472GB in FP16 precision. This vast discrepancy means the entire model cannot be loaded onto the GPU for inference. The RTX 4070 Ti's memory bandwidth of 0.5 TB/s, while respectable, becomes irrelevant in this scenario as the primary bottleneck is the sheer lack of memory capacity. The 7680 CUDA cores and 240 Tensor cores would be underutilized due to the inability to load the model.
Attempting to run DeepSeek-Coder-V2 on the RTX 4070 Ti without significant modifications will result in an out-of-memory error. Even techniques like offloading layers to system RAM would likely lead to unacceptably slow performance due to the constant data transfer between the GPU and system memory. The Ada Lovelace architecture's advancements in Tensor Cores and memory management cannot overcome the fundamental limitation imposed by the 12GB VRAM.
Given the substantial VRAM deficit, directly running DeepSeek-Coder-V2 on an RTX 4070 Ti is impractical. Consider exploring model quantization techniques, such as using 4-bit or 8-bit quantization (e.g., QLoRA, bitsandbytes) to reduce the model's memory footprint. However, even with aggressive quantization, the model size may still exceed the available VRAM, necessitating further compromises. Alternatively, explore distributed inference solutions, which split the model across multiple GPUs. Cloud-based inference services that offer instances with sufficient VRAM (e.g., AWS, Google Cloud, Azure) are also a viable option.
If local inference is a must, consider smaller, more manageable code generation models that fit within the RTX 4070 Ti's memory constraints. Explore fine-tuning smaller models on code-related datasets to achieve comparable performance for specific tasks.