The NVIDIA RTX 3090 Ti, with its 24GB of GDDR6X VRAM and Ampere architecture, is well-suited for running the Gemma 2 27B model, especially when quantized. The Q4_K_M (4-bit) quantization significantly reduces the model's VRAM footprint to approximately 13.5GB. This leaves a substantial 10.5GB VRAM headroom, ensuring the model fits comfortably within the GPU's memory. The 3090 Ti's 1.01 TB/s memory bandwidth is also crucial for feeding data to the GPU's 10752 CUDA cores and 336 Tensor cores efficiently, enabling faster inference speeds. The Ampere architecture's Tensor Cores are specifically designed to accelerate matrix multiplications, which are fundamental operations in deep learning, further enhancing the performance of Gemma 2 27B.
Given the ample VRAM headroom, users can experiment with slightly larger batch sizes if the application allows, although a batch size of 1 is a good starting point for interactive applications. Focus on optimizing the inference framework for the RTX 3090 Ti, exploring options like TensorRT for further performance gains. While Q4_K_M quantization is a good balance between performance and accuracy, consider experimenting with unquantized FP16 or FP8 precisions if even higher accuracy is required and VRAM usage allows. Monitor GPU utilization and temperature during extended inference sessions to ensure thermal limits are not exceeded, as the 3090 Ti has a TDP of 450W.