The primary limiting factor for running large language models like Gemma 2 27B is VRAM. Gemma 2 27B in FP16 (half-precision floating point) requires approximately 54GB of VRAM to load the model and perform inference. The AMD RX 7900 XTX, while a powerful gaming GPU, only offers 24GB of VRAM. This significant deficit of 30GB means the model cannot be loaded in its entirety onto the GPU for processing. The high memory bandwidth of the RX 7900 XTX (0.96 TB/s) would otherwise contribute to faster inference speeds, but this is irrelevant if the model cannot fit in the available memory. The absence of dedicated Tensor Cores on the AMD GPU also means that calculations will be performed on the GPU's compute units, potentially leading to slower performance compared to GPUs with optimized tensor processing capabilities.
Due to the VRAM limitation, running Gemma 2 27B in FP16 on the AMD RX 7900 XTX is not feasible. To make it work, you'll need to drastically reduce the model's memory footprint through quantization. Consider using 4-bit quantization (bitsandbytes or similar) or exploring CPU offloading. With quantization, the model size can be reduced significantly, potentially fitting within the 24GB VRAM. However, expect a trade-off in terms of accuracy and inference speed. If possible, explore using a smaller model variant of Gemma or leverage cloud-based inference services that offer more powerful GPU resources.