The NVIDIA RTX 4090, with its 24GB of GDDR6X VRAM and 1.01 TB/s memory bandwidth, is exceptionally well-suited for running the Gemma 2 2B model. Gemma 2 2B in its INT8 quantized form requires only 2GB of VRAM, leaving a substantial 22GB of headroom. This ample VRAM allows for larger batch sizes and longer context lengths without encountering memory limitations. The RTX 4090's 16384 CUDA cores and 512 Tensor cores further accelerate the model's computations, leading to high throughput. The Ada Lovelace architecture provides significant improvements in tensor core performance, crucial for efficiently processing the matrix multiplications inherent in transformer-based language models like Gemma.
Given the significant VRAM headroom, experiment with increasing the batch size to maximize GPU utilization and throughput. Start with a batch size of 32, as initially estimated, and incrementally increase it until you observe diminishing returns or run into memory constraints. Additionally, explore different context lengths to find a balance between performance and the model's ability to maintain context over longer sequences. Monitor GPU utilization and temperature to ensure the system remains stable, especially when pushing the limits of batch size and context length. Consider using tools like `nvidia-smi` to monitor GPU usage.