The NVIDIA H100 SXM, with its massive 80GB of HBM3 VRAM and 3.35 TB/s memory bandwidth, is an exceptionally powerful GPU, making it ideally suited for a wide range of AI workloads. The BGE-Small-EN model, being a relatively small embedding model with only 0.03B parameters and a modest 0.1GB VRAM footprint in FP16 precision, presents virtually no challenge for the H100. The H100's architecture, based on the Hopper generation, includes 16896 CUDA cores and 528 Tensor cores, providing ample computational resources for rapid inference. This combination ensures that the model can be loaded entirely into the GPU's memory with a significant amount of headroom remaining for larger batch sizes or concurrent deployments of other models.
Given the substantial VRAM headroom, users should explore increasing the batch size to maximize throughput. Experiment with batch sizes larger than the estimated 32 to fully utilize the H100's processing capabilities. Also, consider deploying multiple instances of the BGE-Small-EN model concurrently to serve a larger number of requests in parallel. While FP16 offers a good balance of speed and accuracy, for applications where even higher throughput is crucial, explore using INT8 quantization to potentially further accelerate inference without significant loss in embedding quality. Monitor GPU utilization to ensure optimal performance and avoid bottlenecks.