The NVIDIA RTX 3090, with its 24GB of GDDR6X VRAM, offers ample resources for running the Gemma 2 2B model, especially in its quantized q3_k_m form which requires only 0.8GB of VRAM. This leaves a significant 23.2GB VRAM headroom, allowing for larger batch sizes and potentially multiple model instances. The RTX 3090's high memory bandwidth (0.94 TB/s) ensures fast data transfer between the GPU and memory, crucial for minimizing latency during inference. Furthermore, the 10496 CUDA cores and 328 Tensor Cores provide substantial computational power for accelerating the matrix multiplications and other operations inherent in LLM inference. The Ampere architecture is well-suited for these tasks, offering significant performance gains over previous generations.
Given the substantial VRAM headroom, experiment with increasing the batch size to improve throughput. Start with the estimated batch size of 32 and gradually increase it until you observe performance degradation or encounter memory limitations. Consider using inference frameworks optimized for NVIDIA GPUs, such as TensorRT, to further accelerate the model. Monitor GPU utilization and temperature to ensure the card is operating within safe thermal limits, especially since the RTX 3090 has a TDP of 350W. For optimal performance, ensure you have the latest NVIDIA drivers installed.