The NVIDIA RTX 3090, with its 24GB of GDDR6X VRAM, falls short of the 28GB required to load the Qwen 2.5 14B model in FP16 precision. This VRAM deficit means the model, in its default FP16 configuration, cannot be directly loaded onto the GPU for inference. While the RTX 3090 boasts substantial memory bandwidth (0.94 TB/s), CUDA cores (10496), and Tensor cores (328), these resources cannot be fully utilized if the model exceeds the available VRAM. Attempting to run the model without sufficient VRAM will result in errors, or the system defaulting to CPU usage, which is significantly slower.
To run Qwen 2.5 14B on the RTX 3090, you'll need to implement quantization techniques to reduce the model's memory footprint. Quantization lowers the precision of the model's weights, allowing it to fit within the available VRAM. Consider using 8-bit or even 4-bit quantization. Alternatively, explore offloading some layers to system RAM, although this will substantially reduce inference speed. Finally, you can try using a distributed inference setup across multiple GPUs if available, but this adds significant complexity.