To reduce the memory footprint of Sentence Transformer models during inference or when handling large embeddings, start by optimizing the model itself. Use quantization to convert weights from 32-bit to 16-bit floating-point precision (FP16) or 8-bit integers (INT8). For example, PyTorch supports FP16 inference with model.half(), which cuts memory usage by half with minimal accuracy loss. Libraries like ONNX Runtime or NVIDIA’s TensorRT further optimize quantized models for faster, memory-efficient inference. Additionally, switch to smaller pre-trained models like all-MiniLM-L6-v2 or paraphrase-MiniLM-L3-v2, which retain ~90% of the performance of larger models while using fewer parameters. Distilled or pruned versions of models remove redundant layers or neurons, reducing size without significant performance drops. These optimizations directly shrink the model’s memory footprint during inference.
Next, optimize how embeddings are stored and processed. Convert generated embeddings from FP32 to FP16, which halves their memory requirement. For instance, after computing embeddings with model.encode(text), cast them using .astype(np.float16). For large datasets, use memory-mapped arrays (e.g., NumPy’s memmap) to store embeddings on disk, loading only necessary portions into RAM. Compression techniques like product quantization (via libraries like FAISS) reduce embedding dimensions by clustering values into lower-bit representations. For example, FAISS can compress 768-dimensional FP32 embeddings to 64-byte binary codes, cutting storage by 16x. These methods trade slight accuracy reductions for substantial memory savings, especially useful for applications like semantic search over millions of embeddings.
Finally, streamline inference workflows. Process inputs in smaller batches to avoid loading all data into memory simultaneously. Use dynamic batching—adjusting batch size based on available memory—to balance speed and resource usage. For server deployments, leverage model parallelism or on-demand loading (e.g., with Hugging Face’s accelerate or FastAPI) to keep only active models in memory. Tools like ONNX or OpenVINO convert models to optimized formats, improving memory efficiency during inference. For example, exporting a Sentence Transformer to ONNX reduces runtime overhead by 20-30%. Combining these techniques ensures efficient resource use without compromising scalability for large-scale embedding tasks.