The DeepSeek-Coder-V2 model, with its massive 236 billion parameters, presents a significant challenge for even high-end GPUs like the NVIDIA RTX 6000 Ada. The primary bottleneck lies in the model's VRAM requirement. In FP16 (half-precision floating point), DeepSeek-Coder-V2 needs approximately 472GB of VRAM to load the entire model. The RTX 6000 Ada, while powerful, only offers 48GB of VRAM. This creates a shortfall of 424GB, making it impossible to load the model in its entirety onto the GPU for inference. Consequently, without significant optimization or workarounds, the model cannot run directly on this GPU.
Beyond VRAM, memory bandwidth also plays a crucial role. The RTX 6000 Ada has a memory bandwidth of 0.96 TB/s, which is substantial. However, even with sufficient VRAM, the sheer size of DeepSeek-Coder-V2 would likely lead to performance limitations during inference due to the constant transfer of weights and activations. This would result in slow token generation speeds. Without specialized techniques like quantization and offloading, the model's performance would be far from optimal, even if it could somehow fit within the available VRAM.
Given the substantial VRAM deficit, running DeepSeek-Coder-V2 on a single RTX 6000 Ada is not feasible without employing advanced techniques. Consider exploring quantization methods such as 4-bit or even lower precision to significantly reduce the model's memory footprint. Alternatively, investigate model parallelism across multiple GPUs, which would distribute the model's layers across several devices, effectively increasing the available VRAM. If neither of these options is viable, consider using cloud-based inference services that offer access to GPUs with larger VRAM capacities, or explore smaller, more efficient models that are better suited for your hardware. Another option is CPU offloading, although this will come with a significant performance penalty.
For local experimentation, explore frameworks like `llama.cpp` which are optimized for CPU usage or try using quantized versions of the model that are designed to fit within smaller memory footprints. If you have access to multiple RTX 6000 Ada GPUs, consider using a framework like `vLLM` or `text-generation-inference` to enable model parallelism. Be aware that even with these optimizations, expect significant performance trade-offs compared to running the model on a system with sufficient VRAM.