The primary limiting factor for running DeepSeek-V2.5 (236B parameters) on an AMD RX 7900 XTX is the VRAM. DeepSeek-V2.5, when loaded in FP16 precision, requires approximately 472GB of VRAM. The RX 7900 XTX has only 24GB of VRAM. This creates a massive VRAM headroom deficit of -448GB, meaning the model's weights alone cannot fit onto the GPU. While the RX 7900 XTX boasts a respectable memory bandwidth of 0.96 TB/s, this bandwidth becomes irrelevant when the model exceeds the GPU's memory capacity. The absence of Tensor Cores on the RX 7900 XTX further complicates matters, as these cores are specifically designed to accelerate matrix multiplications, a core operation in deep learning inference.
Given the substantial VRAM discrepancy, running DeepSeek-V2.5 on a single RX 7900 XTX for practical inference is infeasible without significant compromises. Consider using extreme quantization techniques like 4-bit or even 2-bit quantization to drastically reduce the model's memory footprint. Even with quantization, performance will likely be limited due to the need to offload significant portions of the model to system RAM. As an alternative, explore using cloud-based inference services or distributed inference across multiple GPUs with sufficient VRAM. For local execution, consider smaller models that fit within the RX 7900 XTX's VRAM capacity or upgrading to a GPU with significantly more VRAM.