The NVIDIA RTX 3090, while a powerful GPU, falls short of the VRAM requirements for running the Gemma 2 27B model in FP16 precision. The RTX 3090 boasts 24GB of GDDR6X VRAM, while Gemma 2 27B demands approximately 54GB in FP16. This 30GB deficit means the entire model cannot be loaded onto the GPU simultaneously, leading to inevitable out-of-memory errors. The RTX 3090's memory bandwidth of 0.94 TB/s is substantial, but insufficient VRAM negates its potential benefits in this scenario. The Ampere architecture's CUDA and Tensor cores would typically accelerate computations, but the inability to load the model entirely hinders their effectiveness.
Due to the VRAM limitations, directly running Gemma 2 27B on a single RTX 3090 in FP16 is not feasible. The primary recommendation is to explore quantization techniques, such as Q4 or even lower bit precisions, to significantly reduce the model's memory footprint. This can be achieved using frameworks like `llama.cpp` or `text-generation-inference`. Alternatively, consider using a cloud-based solution or a multi-GPU setup with sufficient combined VRAM. If sticking with the RTX 3090, focus on minimizing batch size and context length to potentially squeeze the model into the available memory after quantization, but expect a performance hit.