The primary limiting factor in running large language models (LLMs) like DeepSeek-Coder-V2 is the available VRAM on the GPU. DeepSeek-Coder-V2, with its 236 billion parameters, requires approximately 472GB of VRAM when using FP16 (half-precision floating point) for storing the model weights. The NVIDIA RTX 4080, equipped with 16GB of GDDR6X VRAM, falls significantly short of this requirement. This means the entire model cannot be loaded onto the GPU for inference. Memory bandwidth, while important for performance, is secondary in this scenario, as the model cannot even fit into the available memory. The Ada Lovelace architecture of the RTX 4080 provides strong computational capabilities with its CUDA and Tensor cores, but these cannot be fully utilized when the model exceeds the VRAM capacity.
Due to the substantial VRAM difference, directly running DeepSeek-Coder-V2 on a single RTX 4080 is not feasible without significant modifications. Consider using quantization techniques like 4-bit or even 2-bit quantization to drastically reduce the model's memory footprint. Frameworks like `llama.cpp` are designed for CPU offloading and quantization, which might allow you to run the model, albeit at a significantly reduced speed. Alternatively, explore cloud-based solutions or renting GPUs with sufficient VRAM (e.g., A100, H100) to run the model effectively. Distributed inference across multiple GPUs is another option, but it requires specialized software and adds complexity.