The DeepSeek-Coder-V2 model, with its 236 billion parameters, requires an estimated 472GB of VRAM when using FP16 (half-precision floating point) data types for its weights and activations. The NVIDIA RTX 3060 Ti, equipped with only 8GB of VRAM, falls significantly short of this requirement. This vast difference means the entire model cannot be loaded onto the GPU simultaneously. Running such a large model on a GPU with insufficient VRAM will result in out-of-memory errors, preventing successful inference. Memory bandwidth, while important for performance, becomes a secondary concern when the model size exceeds available memory by such a large margin. The RTX 3060 Ti's 448 GB/s memory bandwidth would be utilized if the model *could* fit, but it cannot.
Due to the severe VRAM limitation, directly running DeepSeek-Coder-V2 on an RTX 3060 Ti is not feasible. Consider using quantization techniques such as 4-bit or even 2-bit quantization to significantly reduce the model's memory footprint. Even with aggressive quantization, the model might still be too large to fit entirely within the 8GB VRAM. In such cases, explore offloading parts of the model to system RAM (CPU) using frameworks like `llama.cpp` or `text-generation-inference` with CPU offloading enabled. Alternatively, consider using cloud-based GPU instances with sufficient VRAM or smaller, more efficient code generation models that fit within your hardware constraints.