The NVIDIA RTX 3090 Ti, with its 24GB of GDDR6X VRAM, faces a challenge when running the Qwen 2.5 72B model, even with quantization. While the model's original FP16 precision demands a hefty 144GB of VRAM, quantizing to q3_k_m reduces this requirement to approximately 28.8GB. However, this still exceeds the RTX 3090 Ti's available VRAM by 4.8GB. This VRAM shortfall will prevent the model from loading and running directly on the GPU. The RTX 3090 Ti's 1.01 TB/s memory bandwidth and substantial CUDA and Tensor core counts would otherwise contribute to decent inference speeds if the model fit within the available memory.
Due to the VRAM limitation, directly running Qwen 2.5 72B (q3_k_m) on the RTX 3090 Ti is not feasible. Consider offloading some layers to system RAM (CPU) using llama.cpp, although this will significantly reduce inference speed. Alternatively, explore using a smaller model variant of Qwen or other models with similar capabilities but lower VRAM footprints. Another option is to utilize cloud-based GPU services that offer instances with sufficient VRAM to accommodate the model.