The DeepSeek-Coder-V2 model, with its 236 billion parameters, presents a significant challenge for the NVIDIA RTX 3080 10GB due to its substantial VRAM requirement. When operating in FP16 (half-precision floating point), DeepSeek-Coder-V2 demands approximately 472GB of VRAM to load the entire model. The RTX 3080, equipped with only 10GB of VRAM, falls drastically short, resulting in a VRAM deficit of 462GB. This incompatibility prevents the model from being loaded and executed directly on the GPU without employing advanced techniques to reduce memory footprint.
Memory bandwidth, while a factor in overall performance, is secondary to the VRAM limitation in this scenario. The RTX 3080's 760 GB/s memory bandwidth is substantial, but irrelevant if the model cannot fit within the available VRAM. The Ampere architecture's Tensor Cores would typically accelerate the matrix multiplications inherent in LLMs, but their potential remains untapped due to the VRAM bottleneck. Without sufficient VRAM, the model cannot be processed, rendering performance metrics like tokens/second and optimal batch size effectively zero.
Given the severe VRAM limitations, directly running DeepSeek-Coder-V2 on an RTX 3080 10GB is not feasible without significant modifications. Model quantization is essential. Consider extreme quantization methods like 4-bit or even 3-bit quantization using libraries like `bitsandbytes` or `AutoGPTQ` to drastically reduce the model's memory footprint. Even with quantization, offloading layers to system RAM (CPU) might be necessary, which will severely impact inference speed.
Alternatively, explore distributed inference solutions, where the model is split across multiple GPUs or machines. Cloud-based inference services that offer pay-per-use GPU resources are another viable option. If local execution is a must, consider using smaller, fine-tuned models that are specifically designed to fit within the RTX 3080's VRAM capacity.