The NVIDIA RTX 4000 Ada, equipped with 20GB of GDDR6 VRAM and the Ada Lovelace architecture, is exceptionally well-suited for running the BGE-Large-EN embedding model. BGE-Large-EN, with its relatively small 0.33B parameters, requires only 0.7GB of VRAM in FP16 precision. This leaves a significant VRAM headroom of 19.3GB, allowing for comfortable operation even with larger batch sizes or when running other applications concurrently. The RTX 4000 Ada's memory bandwidth of 0.36 TB/s is more than sufficient to handle the memory transfer requirements of BGE-Large-EN, ensuring smooth and efficient processing.
Given the ample VRAM available, users can experiment with larger batch sizes to maximize throughput. Starting with a batch size of 32 is a good initial point, but increasing it further may yield even better performance without encountering memory limitations. For optimal performance, consider using an inference framework like `vLLM` or `text-generation-inference`, which are designed for efficient execution of large language models on NVIDIA GPUs. Explore quantization techniques, such as INT8, to potentially further increase the throughput without significant loss of accuracy, although this might not be necessary given the already low memory footprint of the model.