The DeepSeek-Coder-V2 model, with its 236 billion parameters, requires an immense amount of VRAM – approximately 472GB when using FP16 (half-precision floating point) for its weights. The NVIDIA RTX 3060, while a capable card, only offers 12GB of VRAM. This creates a massive shortfall of 460GB. The model's size far exceeds the GPU's capacity, meaning the entire model cannot be loaded onto the RTX 3060 for inference. This incompatibility isn't just about slow performance; it's a fundamental limitation preventing the model from running in its full, unquantized FP16 form.
Due to the substantial VRAM difference, running DeepSeek-Coder-V2 in its original FP16 format on an RTX 3060 12GB is not feasible. To make it runnable, aggressive quantization techniques are necessary. Consider using 4-bit quantization (Q4_K_M or similar) within frameworks like `llama.cpp` or `text-generation-inference`. Even with quantization, performance will be significantly impacted. Alternatively, explore offloading some layers to system RAM, although this will drastically reduce inference speed. For practical use, consider using cloud-based inference services or GPUs with significantly more VRAM (48GB+).