The NVIDIA RTX 3090, with its 24GB of GDDR6X VRAM, 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 of headroom. This ample VRAM allows for larger batch sizes and longer context lengths without encountering memory limitations. Furthermore, the RTX 3090's high memory bandwidth of 0.94 TB/s ensures rapid data transfer between the GPU and memory, crucial for maintaining optimal performance during inference. The 10496 CUDA cores and 328 Tensor Cores within the Ampere architecture provide significant computational power, accelerating the matrix multiplications and other operations inherent in transformer-based language models like Gemma 2.
Given the RTX 3090's capabilities, users can comfortably experiment with larger batch sizes (up to 32 or even higher, depending on the specific inference framework) and the full 8192 token context length offered by Gemma 2 2B. To maximize performance, consider using optimized inference frameworks like `vLLM` or `text-generation-inference`, which are designed to leverage the RTX 3090's architecture efficiently. While FP16 provides a good balance of speed and accuracy, exploring quantization techniques like INT8 might further improve throughput without significant degradation in model quality. If you encounter issues with very large batch sizes, reduce it incrementally until stability is achieved.