The NVIDIA RTX 3090, with its 24GB of GDDR6X VRAM, is exceptionally well-suited for running the Gemma 2 9B model, especially when using INT8 quantization. Quantization reduces the model's memory footprint by representing weights and activations with 8-bit integers instead of higher-precision floating-point numbers. This allows the entire 9B parameter model to fit comfortably within the RTX 3090's VRAM, leaving a substantial 15GB headroom for larger batch sizes, longer context lengths, and other memory-intensive operations. The RTX 3090's high memory bandwidth (0.94 TB/s) is also crucial for efficiently transferring data between the GPU and memory, minimizing bottlenecks during inference.
Furthermore, the RTX 3090's Ampere architecture, featuring 10496 CUDA cores and 328 Tensor Cores, provides significant computational power for accelerating both the forward and backward passes of the neural network. Tensor Cores are specifically designed to accelerate matrix multiplications, which are the fundamental operations in deep learning. The estimated 72 tokens/sec performance is a reasonable expectation, but actual throughput will depend on factors such as the specific inference framework used, batch size, context length, and other optimization techniques.
Given the ample VRAM available, experiment with larger batch sizes to maximize GPU utilization and increase throughput. While INT8 quantization provides excellent memory savings, consider experimenting with FP16 or BF16 precision if higher accuracy is desired, keeping a close eye on VRAM usage. For optimal performance, leverage an inference framework optimized for NVIDIA GPUs, such as TensorRT or vLLM. Monitor GPU utilization and temperature to ensure the RTX 3090 is operating within safe thermal limits, especially when running long inference tasks at high batch sizes. If you encounter any VRAM issues, reduce the batch size or context length.