The NVIDIA RTX 3060 Ti, with its 8GB of GDDR6 VRAM, is an excellent match for the BGE-Small-EN embedding model. BGE-Small-EN, with only 0.03 billion parameters, requires a mere 0.1GB of VRAM when using FP16 (half-precision floating point) data type. This leaves a substantial 7.9GB of VRAM headroom, ensuring the GPU won't be VRAM-constrained. The RTX 3060 Ti's memory bandwidth of 0.45 TB/s further contributes to efficient data transfer between the GPU and memory, crucial for minimizing latency during inference. The Ampere architecture, coupled with 4864 CUDA cores and 152 Tensor Cores, provides ample computational resources for running this relatively small model.
Given the generous VRAM headroom, you can comfortably experiment with larger batch sizes to increase throughput. Start with a batch size of 32, as initially estimated, and gradually increase it until you observe diminishing returns or encounter memory errors. Consider using a high-performance inference framework like ONNX Runtime or TensorRT to further optimize performance. While FP16 is already efficient, exploring INT8 quantization might yield additional speedups with minimal accuracy loss, but this may require careful calibration and validation. Ensure you have the latest NVIDIA drivers installed to take full advantage of the RTX 3060 Ti's capabilities.