The NVIDIA RTX 3090 Ti, with its 24GB of GDDR6X VRAM and 1.01 TB/s memory bandwidth, presents a viable platform for running the Qwen 2.5 32B model, especially when utilizing quantization. The model's original FP16 precision requires 64GB of VRAM, exceeding the RTX 3090 Ti's capacity. However, with q3_k_m quantization, the VRAM footprint is significantly reduced to 12.8GB. This allows the model to fit comfortably within the GPU's memory, leaving a substantial 11.2GB headroom for other processes and preventing out-of-memory errors during inference. The RTX 3090 Ti's 10752 CUDA cores and 336 Tensor Cores contribute to accelerating the computations required for the model's execution, though performance will be limited by the memory bandwidth when dealing with large context lengths.
For optimal performance with the Qwen 2.5 32B model on the RTX 3090 Ti, stick with the q3_k_m quantization. Experimenting with higher quantization levels might further improve performance, but could sacrifice accuracy. It's crucial to monitor VRAM usage during inference to ensure you're not approaching the 24GB limit, especially when using long context lengths. If you encounter performance bottlenecks, try reducing the context length or batch size. Consider using inference frameworks like llama.cpp that are optimized for quantized models and GPU acceleration.