The NVIDIA RTX 3090 Ti, with its 24GB of GDDR6X VRAM and Ampere architecture, is exceptionally well-suited for running the Gemma 2 2B language model. Gemma 2 2B, requiring only 4GB of VRAM in FP16 precision, leaves a substantial 20GB headroom, allowing for larger batch sizes, longer context lengths, and concurrent execution of other tasks. The 3090 Ti's memory bandwidth of 1.01 TB/s ensures rapid data transfer between the GPU and memory, minimizing bottlenecks during model inference. Furthermore, the presence of 10752 CUDA cores and 336 Tensor Cores facilitates efficient parallel processing and accelerated tensor computations, crucial for the matrix multiplications inherent in LLM inference.
For optimal performance, leverage the RTX 3090 Ti's capabilities by exploring larger batch sizes and context lengths. Start with a batch size of 32 and a context length of 8192 tokens, and experiment to find the sweet spot that balances latency and throughput for your specific application. Consider using mixed precision (FP16 or even lower like INT8 with quantization) to further improve inference speed and reduce memory footprint, although this may come with a slight trade-off in accuracy. Regularly monitor GPU utilization and memory usage to identify potential bottlenecks and fine-tune your configuration accordingly. If you're using a framework that supports it, enabling features like CUDA graph capture can also yield performance gains by reducing kernel launch overhead.