The NVIDIA RTX 4070, with its 12GB of GDDR6X VRAM and Ada Lovelace architecture, is an excellent choice for running smaller AI models like BGE-Small-EN. BGE-Small-EN, being a 30 million parameter model, requires a mere 0.1GB of VRAM when using FP16 precision. This leaves a substantial 11.9GB of VRAM headroom, allowing for large batch sizes and potentially running multiple instances of the model concurrently or alongside other applications. The RTX 4070's memory bandwidth of 0.5 TB/s ensures rapid data transfer between the GPU and VRAM, minimizing bottlenecks during inference.
Furthermore, the RTX 4070's 5888 CUDA cores and 184 Tensor cores significantly accelerate the matrix multiplications and other computations crucial for deep learning inference. While BGE-Small-EN is not computationally intensive, these cores contribute to a smooth and responsive user experience. Expect excellent throughput, potentially exceeding 90 tokens per second, depending on the chosen inference framework and batch size. Given the low VRAM footprint, users can experiment with larger batch sizes to maximize GPU utilization and overall performance.
Given the RTX 4070's ample resources, users should focus on maximizing throughput and minimizing latency. Experiment with different batch sizes to find the optimal balance for your specific application. For example, start with a batch size of 32 and gradually increase it until you observe diminishing returns in terms of tokens per second. Also, consider using an optimized inference framework like ONNX Runtime or TensorRT to further accelerate the model.
While FP16 precision works well, exploring lower precision formats like INT8 quantization could potentially boost performance even further, albeit with a possible slight reduction in accuracy. However, for embedding models, the impact of quantization is often negligible. Ensure your drivers are up to date to take advantage of the latest performance improvements and bug fixes. The large VRAM headroom means you can easily run other tasks simultaneously without impacting the model's performance.