The NVIDIA RTX 3090 Ti, while a powerful GPU, falls short of the VRAM requirements for running the Qwen 2.5 32B model in FP16 precision. Qwen 2.5 32B requires approximately 64GB of VRAM to load and operate effectively in FP16. The RTX 3090 Ti only offers 24GB of GDDR6X VRAM. This 40GB deficit means the model cannot be loaded entirely onto the GPU, leading to out-of-memory errors or forcing the system to rely on significantly slower system RAM, which will severely degrade performance. The 3090 Ti's memory bandwidth of 1.01 TB/s is substantial, but irrelevant if the model cannot fit within the available VRAM.
To run Qwen 2.5 32B on an RTX 3090 Ti, you will need to implement aggressive quantization techniques to reduce the model's memory footprint. Consider using 4-bit quantization (bitsandbytes or GPTQ) or even lower precision formats like 3-bit. This will significantly decrease the VRAM requirement, potentially bringing it within the 24GB limit. Another approach is to offload some layers to system RAM, but this will dramatically reduce inference speed. Alternatively, explore using cloud-based GPU services or upgrading to a GPU with more VRAM, such as an A100 or H100.