The NVIDIA RTX 4090, with its 24GB of GDDR6X VRAM, falls short of the 36GB VRAM requirement for running Qwen 2.5 72B (72.00B) quantized to Q4_K_M. This means the entire model cannot be loaded onto the GPU, preventing successful inference. While the RTX 4090 boasts a high memory bandwidth of 1.01 TB/s and a substantial number of CUDA and Tensor cores, these specifications become irrelevant when the model exceeds the available VRAM. Attempting to run the model in this configuration will result in errors, as the system will be unable to allocate the necessary memory for the model's weights and activations.
Due to the VRAM limitations, directly running Qwen 2.5 72B (72.00B) on a single RTX 4090 is not feasible. Consider exploring model parallelism across multiple GPUs if available, which involves splitting the model across several GPUs to distribute the VRAM load. Alternatively, explore further quantization options, such as Q2 or even lower precisions, which might reduce the VRAM footprint at the cost of some accuracy. Finally, for single RTX 4090 usage, focus on smaller models with parameter counts that fit within the 24GB VRAM limit.