The NVIDIA RTX 3090 Ti, with its 24GB of GDDR6X VRAM and 1.01 TB/s memory bandwidth, is a powerful card, but it falls short of the VRAM requirements for running Gemma 2 27B in INT8 quantization. While INT8 reduces the VRAM footprint compared to FP16 (requiring 54GB), it still needs 27GB of VRAM. The 3090 Ti's 24GB leaves a 3GB deficit, preventing the model from loading entirely onto the GPU. This limitation directly impacts the ability to perform inference. The 10752 CUDA cores and 336 Tensor cores would otherwise provide substantial computational power for accelerating the model, but are bottlenecked by the VRAM constraint. The Ampere architecture is well-suited for AI tasks, but cannot overcome the fundamental memory limitation in this scenario.
To run Gemma 2 27B on the RTX 3090 Ti, you'll need to explore aggressive quantization techniques beyond INT8. Consider using 4-bit quantization (INT4) or even mixed-precision quantization. This will significantly reduce the VRAM footprint, potentially bringing it within the 3090 Ti's 24GB capacity. However, be aware that extreme quantization can impact model accuracy. Alternatively, explore offloading some layers to system RAM (CPU), which will significantly reduce inference speed. If you are willing to invest in more hardware, consider using multiple GPUs to distribute the model's layers across them.