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-M3 embedding model. BGE-M3, a relatively small model with only 0.5 billion parameters, requires a mere 1GB of VRAM in FP16 precision. This leaves a substantial 79GB of VRAM headroom, allowing for large batch sizes, concurrent model serving, or the simultaneous operation of other AI workloads. The H100's Hopper architecture, featuring 14592 CUDA cores and 456 Tensor Cores, provides ample computational power for accelerating the matrix multiplications and other operations inherent in BGE-M3, resulting in high throughput and low latency.
Given the vast resources available on the H100, users should prioritize maximizing throughput by increasing the batch size. Experiment with batch sizes up to 32 or even higher, monitoring VRAM usage to ensure optimal utilization without exceeding capacity. Furthermore, explore inference frameworks like vLLM or NVIDIA's TensorRT to further optimize performance. While BGE-M3 is already a compact model, consider quantization techniques like INT8 or even lower precisions if latency is a critical factor, although the performance gains might be minimal given the H100's inherent power.