The NVIDIA RTX 3090 Ti, equipped with 24GB of GDDR6X VRAM and a memory bandwidth of 1.01 TB/s, faces a significant challenge when running the Qwen 2.5 72B model. This large language model requires approximately 144GB of VRAM when using FP16 precision. The deficit of 120GB between the model's VRAM requirement and the GPU's capacity means that the model cannot be loaded and executed directly on the RTX 3090 Ti without employing specific techniques to reduce memory footprint. The 10752 CUDA cores and 336 Tensor cores of the RTX 3090 Ti would be sufficient for processing the model if it could fit in memory, but the memory limitation is the primary bottleneck.
To run Qwen 2.5 72B on the RTX 3090 Ti, aggressive quantization techniques are essential. Consider using 4-bit or even 3-bit quantization methods offered by libraries like `llama.cpp` or `AutoGPTQ`. Furthermore, offloading some layers to system RAM is an option, although this will significantly reduce inference speed. Explore distributed inference across multiple GPUs if feasible, or consider using cloud-based GPU resources with sufficient VRAM to avoid these limitations. Without these optimizations, running the model locally on the RTX 3090 Ti is not practically viable.