The NVIDIA RTX 3080 12GB is a high-performance consumer GPU based on the Ampere architecture. It boasts 8960 CUDA cores and 280 Tensor cores, providing substantial computational power for various AI tasks. However, its primary limitation when running extremely large language models like DeepSeek-Coder-V2 is its 12GB of GDDR6X VRAM. 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. This means the RTX 3080 12GB falls significantly short, lacking 460GB of the necessary memory to load the model in FP16.
Memory bandwidth is also a factor, though secondary to the VRAM constraint. The RTX 3080 12GB offers 912 GB/s of memory bandwidth, which is excellent. However, even with sufficient bandwidth, the inability to load the entire model into VRAM renders the bandwidth largely irrelevant. Without model parallelism or offloading techniques, running DeepSeek-Coder-V2 directly on the RTX 3080 12GB is not feasible. The expected performance without these workarounds would be zero tokens per second, as the model simply cannot be loaded.
Due to the severe VRAM limitations, directly running DeepSeek-Coder-V2 on a single RTX 3080 12GB is not possible without significant modifications. Consider exploring quantization techniques like 4-bit or even 2-bit quantization to drastically reduce the model's memory footprint. Frameworks like `llama.cpp` are optimized for CPU inference and quantization, potentially allowing you to run a heavily quantized version of the model, albeit with reduced performance, on your CPU in conjunction with the GPU. Alternatively, investigate model parallelism, which involves splitting the model across multiple GPUs, or offloading some layers to system RAM. However, these approaches require significant technical expertise and may still result in slow inference speeds.
If high performance is a priority, consider using cloud-based inference services or investing in GPUs with significantly more VRAM, such as the NVIDIA A100 or H100, or utilizing multiple high-end GPUs in a server environment. These options provide the necessary resources to run large language models like DeepSeek-Coder-V2 efficiently.