The NVIDIA RTX 3090 Ti, with its 24GB of GDDR6X VRAM and Ampere architecture, is exceptionally well-suited for running the Gemma 2 9B language model, particularly when utilizing INT8 quantization. Gemma 2 9B in INT8 requires approximately 9GB of VRAM, leaving a substantial 15GB headroom on the 3090 Ti. This ample VRAM allows for larger batch sizes and longer context lengths without exceeding the GPU's memory capacity. Furthermore, the 3090 Ti's memory bandwidth of 1.01 TB/s ensures rapid data transfer between the GPU and memory, minimizing bottlenecks during inference. The presence of 10752 CUDA cores and 336 Tensor cores further accelerates the computations involved in running the model.
The Ampere architecture's optimized Tensor Cores are particularly beneficial for accelerating matrix multiplications, a core operation in deep learning inference. While FP16 precision would require 18GB of VRAM, INT8 quantization not only halves the memory footprint but also often improves inference speed due to increased throughput on the Tensor Cores. The estimated tokens/sec of 72 reflects a strong performance profile, enabled by the GPU's robust specifications and the model's efficient design. Larger models can be loaded, but the 9B parameter model is a sweet spot for this GPU.
Given the comfortable VRAM headroom, users should prioritize maximizing batch size and context length to optimize throughput. Experimenting with different inference frameworks like `llama.cpp`, `vLLM`, or `text-generation-inference` can yield further performance gains. Consider using the `AWQ` quantization method for a balance between speed and accuracy. Monitor GPU utilization during inference to identify potential bottlenecks and adjust settings accordingly. While the model runs well in INT8, evaluate the trade-off between speed and accuracy when switching to FP16.