The NVIDIA RTX 4060 Ti 16GB is exceptionally well-suited for running the BGE-Small-EN embedding model. With 16GB of GDDR6 VRAM, it far exceeds the model's modest 0.1GB requirement, leaving a substantial 15.9GB headroom. This ample VRAM allows for large batch sizes and parallel processing, maximizing GPU utilization. The RTX 4060 Ti's Ada Lovelace architecture, featuring 4352 CUDA cores and 136 Tensor cores, provides significant computational power for efficient tensor operations, crucial for embedding generation. While the memory bandwidth of 0.29 TB/s isn't the highest available, it's more than sufficient for a model of this size, ensuring that data transfer doesn't become a bottleneck during inference.
Given the large VRAM headroom, experiment with larger batch sizes to improve throughput. A batch size of 32 is a good starting point, but you may be able to increase it further depending on your system's memory and processing capabilities. Consider using a high-performance inference framework like ONNX Runtime or TensorRT to optimize the model for your specific hardware. Explore quantization techniques, even though the model is already small, as it can potentially improve inference speed with minimal impact on accuracy.