The NVIDIA RTX 5000 Ada, while a powerful professional GPU, falls short of the VRAM requirements for running DeepSeek-Coder-V2. DeepSeek-Coder-V2, with its 236 billion parameters, necessitates a substantial 472GB of VRAM when using FP16 precision. The RTX 5000 Ada only provides 32GB of GDDR6 memory. This creates a significant VRAM deficit of 440GB, preventing the model from loading entirely onto the GPU for inference. Consequently, without employing advanced techniques like quantization or offloading, the model cannot be executed on this GPU. Memory bandwidth, while respectable at 0.58 TB/s, becomes a secondary bottleneck since the primary issue is insufficient memory capacity.
Due to the VRAM limitation, direct inference is impossible. Even if techniques like CPU offloading were attempted, the performance would be severely impacted. The model's size would cause constant data transfer between system RAM and GPU memory, dramatically reducing the tokens/second processed. Batch size would also be severely limited, likely to the point of being unusable for real-time applications. In practical terms, the RTX 5000 Ada simply lacks the memory footprint to handle the entire DeepSeek-Coder-V2 model in FP16.
Given the significant VRAM shortfall, running DeepSeek-Coder-V2 directly on the RTX 5000 Ada is not feasible without substantial modifications. The most viable option is to explore aggressive quantization techniques, such as converting the model to 4-bit or even 3-bit precision. This would drastically reduce the VRAM footprint, potentially bringing it within the RTX 5000 Ada's capabilities, although significant performance degradation is still expected. Alternatively, consider using cloud-based inference services or upgrading to a GPU with significantly more VRAM (e.g., NVIDIA A100, H100) to achieve acceptable performance.
If you decide to pursue quantization, use a framework like `llama.cpp` or `ExLlamaV2` that is optimized for low-precision inference. Be prepared to experiment with different quantization methods and configurations to find a balance between VRAM usage and output quality. Furthermore, investigate techniques like model parallelism, where the model is split across multiple GPUs, although this would require a different hardware setup.