Using a GPU instead of a CPU significantly accelerates sentence encoding with Sentence Transformer models due to the GPU’s ability to parallelize the computationally intensive operations involved. Sentence Transformers, which are built on transformer architectures like BERT, rely heavily on matrix multiplications and attention mechanisms. These operations are inherently parallelizable, making GPUs—with their thousands of smaller, efficient cores—ideally suited for the task. In contrast, CPUs, which prioritize sequential processing with fewer, more generalized cores, struggle to handle the massive number of simultaneous calculations required, leading to slower encoding times.
The performance gap becomes especially pronounced when processing batches of sentences. GPUs excel at batched operations because they can process multiple data points in parallel, leveraging their high memory bandwidth and specialized hardware (e.g., Tensor Cores in NVIDIA GPUs) to accelerate matrix operations. For example, encoding 1,000 sentences in a batch on a GPU might take a few seconds, while a CPU could require minutes. Additionally, frameworks like PyTorch or TensorFlow optimize GPU usage by minimizing data transfer overhead and maximizing computational throughput. CPUs, even with optimizations like multithreading via OpenMP, cannot match this efficiency due to architectural limitations in memory bandwidth and parallelism. Smaller batch sizes or single-sentence encoding may reduce the GPU’s advantage, but real-world use cases typically involve batch processing, where GPUs dominate.
A practical example illustrates the difference: encoding 10,000 sentences using the all-mpnet-base-v2 model might take 2-3 seconds on a modern GPU like an NVIDIA A100 but over 10 minutes on a high-end CPU. This disparity grows with model size or sequence length. For developers, this means GPUs are essential for production-scale applications requiring low latency, such as real-time semantic search or large-scale clustering. While CPUs remain viable for small-scale testing or environments without GPU access, their limitations in parallel processing make them impractical for most Sentence Transformer workloads. The choice ultimately hinges on balancing task scale, latency requirements, and infrastructure constraints.