The NVIDIA H100 SXM, with its 80GB of HBM3 memory and Hopper architecture, 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 on the H100, ensuring that memory constraints will not be a bottleneck. The H100's impressive 3.35 TB/s memory bandwidth further accelerates data transfer, crucial for efficient model execution.
The Hopper architecture's Tensor Cores are specifically designed to accelerate matrix multiplications, a core operation in deep learning models like BGE-M3. This, combined with the ample VRAM and high memory bandwidth, allows the H100 to process inference requests at a high throughput. The estimated tokens per second (135) reflects the rapid processing capability, making the H100 an ideal choice for real-time embedding generation. Furthermore, the large VRAM allows for a large batch size of 32, further improving throughput.
Given the significant VRAM headroom, explore increasing the batch size to further optimize throughput. Experiment with different inference frameworks like vLLM or Text Generation Inference, which are designed for high-throughput serving. While FP16 provides a good balance of speed and accuracy, consider mixed precision training (e.g., using bfloat16 where supported) for potential performance gains without significant accuracy loss. Monitor GPU utilization to ensure that the model is fully utilizing the H100's resources; if utilization is low, further increase the batch size or explore parallel inference strategies.
If you encounter any performance bottlenecks, profile the application to identify the specific areas that are causing slowdowns. Common bottlenecks include data loading, pre-processing, or post-processing. Optimizing these aspects can significantly improve overall performance. Additionally, ensure that you are using the latest NVIDIA drivers and CUDA toolkit for optimal performance and compatibility.