The NVIDIA RTX 6000 Ada, while a powerful professional GPU, falls short of the VRAM requirements for running DeepSeek-V2.5. DeepSeek-V2.5, with its 236 billion parameters, necessitates approximately 472GB of VRAM when using FP16 (half-precision floating point) for storing the model weights. The RTX 6000 Ada provides only 48GB of VRAM, resulting in a substantial VRAM deficit of 424GB. This means the entire model cannot reside on the GPU's memory simultaneously, leading to out-of-memory errors or requiring complex and significantly slower offloading techniques.
Memory bandwidth, while important, is secondary to VRAM capacity in this scenario. The RTX 6000 Ada's 0.96 TB/s memory bandwidth would be sufficient *if* the model fit in VRAM. However, because the model significantly exceeds the available VRAM, the GPU will be forced to constantly swap data between the system RAM and the GPU, severely impacting performance. Without sufficient VRAM to hold the model, achieving reasonable inference speeds is virtually impossible. Expect extremely low tokens/second and severely limited batch sizes if attempting to run the model without significant modifications.
Running DeepSeek-V2.5 directly on a single RTX 6000 Ada is not feasible due to the massive VRAM requirement. Consider using model quantization techniques, such as converting the model to INT8 or even lower precision like INT4, to significantly reduce the VRAM footprint. However, even with quantization, it is unlikely to fit the entire model within the 48GB of VRAM. Distributed inference across multiple GPUs is a more viable option if you need to run the full model. Alternatively, explore smaller, fine-tuned versions of similar models that can fit within the RTX 6000 Ada's VRAM.
If you still want to experiment with DeepSeek-V2.5 on the RTX 6000 Ada, focus on extreme quantization, CPU offloading (expect very slow performance), and very small batch sizes. Prioritize using an inference framework optimized for low-resource environments. If you are not tied to DeepSeek-V2.5, consider using a smaller LLM.