The NVIDIA H100 PCIe, with its massive 80GB of HBM2e VRAM and 2.0 TB/s memory bandwidth, is exceptionally well-suited for running the BGE-Large-EN embedding model. BGE-Large-EN, with only 0.33 billion parameters, requires a mere 0.7GB of VRAM when using FP16 precision. This leaves a substantial 79.3GB of VRAM headroom, allowing for significant batch processing, concurrent model serving, or the deployment of other models alongside BGE-Large-EN. The H100's Hopper architecture, featuring 14592 CUDA cores and 456 Tensor Cores, further accelerates the model's computations, ensuring low latency and high throughput.
Given the ample VRAM available on the H100, users should prioritize maximizing batch size to improve overall throughput. Experiment with batch sizes up to the estimated limit of 32, and monitor GPU utilization to find the optimal balance between latency and throughput. Consider using a high-performance inference framework like vLLM or NVIDIA's TensorRT to further optimize performance. While FP16 precision is sufficient for BGE-Large-EN, exploring lower precision options like INT8 or even quantization-aware training might yield further performance gains with minimal accuracy loss.