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-Small-EN embedding model. BGE-Small-EN, being a relatively small model with only 0.03 billion parameters, requires a mere 0.1GB of VRAM in FP16 precision. This leaves an enormous VRAM headroom of 79.9GB, meaning the H100 can comfortably handle multiple instances of the model simultaneously or be used for other tasks concurrently without encountering memory constraints. The H100's Hopper architecture, featuring 14592 CUDA cores and 456 Tensor cores, further accelerates the model's computations.
The H100's high memory bandwidth ensures rapid data transfer between the GPU and its memory, preventing bottlenecks during inference. Even with a small model like BGE-Small-EN, the H100's architecture will significantly contribute to low latency and high throughput. The estimated tokens/sec of 117 indicates a fast inference speed, and a batch size of 32 can be used to further optimize throughput. The large VRAM headroom enables experimentation with larger batch sizes if desired, potentially leading to even higher throughput.
Given the vast resources of the H100 relative to the model's requirements, performance is unlikely to be limited by the GPU itself. Instead, optimization efforts should focus on the software stack, including the choice of inference framework and batching strategies. The high core count of the H100 also allows for easy parallelization of inference requests.
For optimal performance, leverage an efficient inference framework such as vLLM or NVIDIA's TensorRT. Experiment with increasing the batch size beyond 32 to maximize throughput, keeping in mind the context length limitations of 512 tokens. Monitor GPU utilization to ensure the H100 is being fully utilized; if utilization is low, consider running multiple instances of the model or using the GPU for other tasks concurrently.
While FP16 precision is sufficient for BGE-Small-EN, consider experimenting with INT8 quantization to potentially further improve throughput with minimal impact on accuracy. Use profiling tools to identify any bottlenecks in the inference pipeline, such as data loading or pre/post-processing steps, and optimize those accordingly. Finally, consider using a dedicated inference server like NVIDIA Triton Inference Server to manage and scale your BGE-Small-EN deployment.