The NVIDIA RTX 3090 Ti, while a powerful GPU, falls short when running the Gemma 2 27B model due to insufficient VRAM. Gemma 2 27B in FP16 precision requires approximately 54GB of VRAM to load the model and perform inference. The RTX 3090 Ti is equipped with 24GB of GDDR6X memory. This results in a VRAM deficit of 30GB, making it impossible to load the entire model onto the GPU without employing significant offloading techniques. Memory bandwidth, while substantial at 1.01 TB/s on the 3090 Ti, becomes less of a bottleneck when the model cannot fully reside in the GPU's memory.
To run Gemma 2 27B on the RTX 3090 Ti, you'll need to leverage quantization and offloading techniques. Start with aggressive quantization, such as Q4_K_S or even lower, using llama.cpp or similar frameworks. Model splitting, where parts of the model are offloaded to system RAM or even disk, can also help, but will significantly degrade performance. Consider exploring smaller models or using cloud-based solutions if real-time inference is crucial. If possible, consider upgrading to a GPU with more VRAM.