The NVIDIA RTX 3090 Ti, with its substantial 24GB of GDDR6X VRAM, is exceptionally well-suited for running the Gemma 2 9B model, especially when quantized. The Q4_K_M quantization significantly reduces the model's memory footprint to approximately 4.5GB. This leaves a considerable 19.5GB VRAM headroom, ensuring smooth operation even with larger context lengths and batch sizes. The RTX 3090 Ti's impressive memory bandwidth of 1.01 TB/s further contributes to efficient data transfer between the GPU and memory, minimizing potential bottlenecks during inference.
Furthermore, the Ampere architecture of the RTX 3090 Ti, featuring 10752 CUDA cores and 336 Tensor cores, provides ample computational power for accelerating Gemma 2 9B's matrix multiplications and other operations. The Tensor cores are particularly beneficial for accelerating quantized inference. This combination of high VRAM, memory bandwidth, and computational resources translates into excellent performance, as indicated by the estimated 72 tokens/sec. This throughput makes interactive applications and real-time text generation feasible.
Given the ample VRAM available, experiment with increasing the batch size to further improve throughput. While the Q4_K_M quantization provides a good balance of performance and memory usage, consider experimenting with unquantized FP16 precision if you require maximum accuracy and have sufficient VRAM for the larger model size (approximately 18GB). If you are using llama.cpp, ensure you are using the latest version to take advantage of the latest optimizations. Also, monitor GPU utilization during inference to identify potential bottlenecks and adjust settings accordingly.
If you encounter performance issues, verify that the GPU drivers are up to date and that the inference framework (e.g., llama.cpp) is properly configured to utilize the GPU. Consider using a more optimized inference framework like vLLM or text-generation-inference if you require even higher throughput, especially for production deployments. For optimal performance, ensure the model and data are loaded into VRAM before starting inference.