The NVIDIA A100 40GB GPU is exceptionally well-suited for running the BGE-Small-EN embedding model. BGE-Small-EN, with its 0.03 billion parameters, has a very modest VRAM footprint of approximately 0.1GB when using FP16 precision. The A100's substantial 40GB of HBM2e memory provides an enormous headroom of 39.9GB, ensuring that VRAM limitations will not be a bottleneck. The A100's high memory bandwidth of 1.56 TB/s ensures rapid data transfer between the GPU and memory, further enhancing performance.
Given the A100's Ampere architecture, featuring 6912 CUDA cores and 432 Tensor Cores, BGE-Small-EN can leverage these resources for highly efficient inference. The Tensor Cores, in particular, are optimized for matrix multiplication operations which are fundamental to deep learning, enabling faster and more power-efficient computations. The combination of ample VRAM, high memory bandwidth, and specialized hardware acceleration makes the A100 an ideal platform for deploying BGE-Small-EN at scale.
Based on the specifications, we estimate the A100 can achieve approximately 117 tokens per second with a batch size of 32. This figure is an estimation and will vary based on the specific inference framework and optimization techniques employed. The A100’s power consumption (TDP of 400W) should also be considered within the overall system design, particularly for high-throughput applications.
For optimal performance with BGE-Small-EN on the NVIDIA A100 40GB, utilize an optimized inference framework such as vLLM or Hugging Face's Transformers library with appropriate hardware acceleration. Experiment with different batch sizes to maximize throughput, keeping in mind that larger batch sizes may increase latency but can improve overall efficiency. Monitor GPU utilization and memory usage to fine-tune settings for your specific workload.
Given the low memory footprint of BGE-Small-EN, consider running multiple instances of the model concurrently on the A100 to further increase throughput. You may also explore quantization techniques, such as INT8, to potentially reduce memory bandwidth requirements and improve inference speed, although the gains may be minimal due to the model's small size and the A100's already high bandwidth.