The NVIDIA RTX 4090, with its 24GB of GDDR6X VRAM, falls short of the 64GB VRAM requirement for running the Qwen 2.5 32B model in FP16 precision. This discrepancy means the entire model cannot be loaded onto the GPU simultaneously, leading to a 'FAIL' verdict for direct compatibility. While the RTX 4090 boasts a high memory bandwidth of 1.01 TB/s and substantial compute power with 16384 CUDA cores and 512 Tensor cores, these specifications are irrelevant if the model exceeds the available memory. Attempting to run the model without addressing the VRAM issue will result in out-of-memory errors, preventing successful inference.
To run Qwen 2.5 32B on an RTX 4090, you'll need to significantly reduce the model's memory footprint. The primary method is quantization, specifically using 4-bit or 8-bit quantization. This reduces the memory required per parameter, potentially bringing the model within the 24GB VRAM limit. Consider using inference frameworks like llama.cpp or vLLM, which offer efficient quantization and memory management features. Offloading some layers to system RAM ('CPU offloading') is another option, but it will severely impact performance due to the slower transfer speeds between system RAM and the GPU. If acceptable performance isn't achievable with quantization and CPU offloading, consider using a GPU with more VRAM or splitting the model across multiple GPUs.