The NVIDIA RTX 4090, with its 24GB of GDDR6X VRAM and Ada Lovelace architecture, is exceptionally well-suited for running the Gemma 2 9B model, especially when quantized. The q3_k_m quantization brings the model's VRAM footprint down to a mere 3.6GB, leaving a substantial 20.4GB of VRAM headroom. This large VRAM buffer allows for comfortable operation, enabling larger batch sizes and longer context lengths without encountering memory limitations. The RTX 4090's impressive memory bandwidth of 1.01 TB/s ensures rapid data transfer between the GPU and memory, minimizing latency and maximizing throughput during inference. The Ada Lovelace architecture's Tensor Cores are also leveraged to accelerate the matrix multiplications inherent in transformer models like Gemma, further boosting performance.
Given the ample VRAM headroom, users should experiment with increasing the batch size to maximize GPU utilization and throughput. Starting with a batch size of 11, as estimated, is a good baseline, but pushing it higher could significantly improve tokens/sec. Additionally, while q3_k_m quantization provides excellent VRAM savings, consider experimenting with slightly higher quantization levels (e.g., q4_k_m) if performance allows, as this might improve output quality without significantly impacting VRAM usage. Ensure you're using the latest NVIDIA drivers and a compatible inference framework like `llama.cpp` or `vLLM` to take full advantage of the RTX 4090's capabilities.