The NVIDIA RTX 4090, with its 24GB of GDDR6X VRAM, falls short of the VRAM needed to run the Gemma 2 27B model, even when quantized to INT8. Gemma 2 27B requires 27GB of VRAM in its INT8 quantized form. The RTX 4090's 1.01 TB/s memory bandwidth is excellent and would normally facilitate fast data transfer between the GPU and memory, but the primary bottleneck here is the insufficient VRAM capacity. While the 4090's CUDA and Tensor cores are powerful, they cannot compensate for the inability to load the entire model into GPU memory. This VRAM deficit will prevent the model from running or cause it to crash due to out-of-memory errors.
Due to the RTX 4090's 24GB VRAM limitation, running Gemma 2 27B even in INT8 quantization is not feasible. Consider using a lower-parameter model, such as Gemma 2 9B, which will fit within the available VRAM. Alternatively, explore cloud-based solutions or GPUs with more VRAM, such as the RTX 6000 Ada Generation or NVIDIA A100. Model parallelism, where the model is split across multiple GPUs, is another option, but it introduces significant complexity. If using a smaller model, llama.cpp with appropriate quantization settings is a good starting point for local inference.