The NVIDIA RTX 3090 Ti, while a powerful GPU with 10752 CUDA cores and 24GB of GDDR6X VRAM, falls short of the VRAM requirement for running Qwen 2.5 72B, even with aggressive quantization. The Q4_K_M quantization reduces the model's footprint to approximately 36GB, but this still exceeds the 3090 Ti's available 24GB. This VRAM deficit will prevent the model from loading and running effectively, as the entire model and its working memory cannot fit within the GPU's memory.
Memory bandwidth, while substantial at 1.01 TB/s on the RTX 3090 Ti, is secondary to the VRAM limitation in this scenario. Even with sufficient bandwidth, the GPU cannot process data it cannot store. The Ampere architecture's Tensor Cores would be beneficial for accelerating matrix multiplications during inference, but they remain unusable without the necessary memory capacity. The high TDP of 450W also becomes a non-issue since the model will likely not be able to run.
Unfortunately, running Qwen 2.5 72B on a single RTX 3090 Ti is not feasible due to the VRAM limitation. Consider using a GPU with at least 36GB of VRAM or exploring alternative solutions like offloading layers to system RAM. Offloading will significantly degrade performance, making it unsuitable for real-time applications. Another option is to use a smaller model variant that fits within the available VRAM or use distributed inference across multiple GPUs, if possible.