The NVIDIA RTX 3080 12GB, while a powerful card, falls significantly short of the VRAM requirements for running DeepSeek-V2.5 in its native FP16 precision. DeepSeek-V2.5, with its 236 billion parameters, necessitates approximately 472GB of VRAM when using FP16 (half-precision floating point). The RTX 3080 12GB only provides 12GB of VRAM, leaving a deficit of 460GB. This massive discrepancy means the entire model cannot be loaded onto the GPU at once for inference. While the RTX 3080's 0.91 TB/s memory bandwidth and 8960 CUDA cores would contribute to reasonable inference speeds if the model *could* fit, the VRAM limitation is a hard constraint.
Even with substantial optimizations, the full DeepSeek-V2.5 model cannot be effectively run on a single RTX 3080 12GB. Techniques like offloading layers to system RAM (CPU) would introduce significant latency due to the slower transfer speeds between the GPU and system memory. This would severely bottleneck performance, rendering the model unusable for real-time or interactive applications. Furthermore, the model's context length of 128,000 tokens exacerbates the VRAM demands, as larger context windows require more memory to store the attention mechanism's intermediate calculations.
Given the VRAM limitations, directly running DeepSeek-V2.5 on an RTX 3080 12GB is not feasible. You would need to explore model quantization techniques to significantly reduce the VRAM footprint. Consider using a framework like `llama.cpp` with aggressive quantization (e.g., Q4_K_M or even lower) to potentially squeeze a highly compressed version of the model into the available VRAM. However, expect a substantial reduction in model quality and accuracy.
Alternatively, consider cloud-based inference services or platforms that offer access to GPUs with sufficient VRAM, such as NVIDIA A100 or H100 instances. Another approach is to explore distributed inference solutions, where the model is split across multiple GPUs, though this requires significant technical expertise and infrastructure. If you intend to use a local setup, consider upgrading to a GPU with significantly more VRAM, or exploring smaller, more manageable LLMs that fit within the RTX 3080's memory capacity.