The NVIDIA RTX 3090 Ti, with its 24GB of GDDR6X VRAM and Ampere architecture, offers excellent compatibility with the Gemma 2 9B model, particularly when using quantization. The q3_k_m quantization significantly reduces the model's VRAM footprint to approximately 3.6GB. This leaves a substantial 20.4GB of VRAM headroom, allowing for comfortable operation and potentially enabling larger batch sizes or concurrent model instances. The RTX 3090 Ti's memory bandwidth of 1.01 TB/s ensures rapid data transfer between the GPU and memory, further enhancing performance. The presence of 10752 CUDA cores and 336 Tensor Cores also contributes to accelerating the model's computations, especially when leveraging Tensor Cores for optimized matrix multiplications.
Given the ample VRAM headroom, users should experiment with increasing the batch size to maximize throughput. A starting point of 11, as estimated, is a good baseline, but higher batch sizes may be achievable without encountering memory limitations. Consider using an inference framework like `llama.cpp` or `vLLM` to take advantage of optimized kernels and memory management. Monitoring GPU utilization and temperature is crucial, especially given the RTX 3090 Ti's high TDP of 450W. Ensure adequate cooling to prevent thermal throttling and maintain consistent performance. For more demanding applications, consider using techniques like speculative decoding to further improve tokens/second.