The NVIDIA RTX 3090, with its 24GB of GDDR6X VRAM, falls short of the 27GB VRAM needed to run the INT8 quantized Gemma 2 27B model. This means the entire model cannot be loaded onto the GPU's memory, leading to a compatibility failure. While the RTX 3090 boasts a high memory bandwidth of 0.94 TB/s and a substantial number of CUDA and Tensor cores, these specifications become irrelevant when the model exceeds the available VRAM. Attempting to run the model in this configuration will likely result in out-of-memory errors, preventing successful inference.
To run Gemma 2 27B on the RTX 3090, you'll need to explore more aggressive quantization techniques. Consider using a 4-bit quantization method (Q4) which can significantly reduce the VRAM footprint. Alternatively, you could explore offloading some layers of the model to system RAM, but this will drastically reduce inference speed due to the slower transfer rates between system RAM and the GPU. If possible, consider upgrading to a GPU with more VRAM or distributing the model across multiple GPUs if feasible and supported by your chosen inference framework.