To scale embedding generation for millions of documents, you need a combination of efficient hardware use, parallel processing, and optimized workflows. Start by leveraging GPUs or TPUs, which drastically speed up neural network computations compared to CPUs. For example, a single NVIDIA A100 GPU can process thousands of text samples per minute using models like BERT or Sentence-BERT. Use frameworks like PyTorch or TensorFlow, which support batch processing—this lets you generate embeddings for multiple documents simultaneously. Set a batch size that maximizes GPU memory without causing out-of-memory errors (e.g., 64–128 documents per batch). Tools like Hugging Face’s transformers library simplify this with built-in pipelines for batched inference.
Next, distribute the workload across multiple machines. If you’re working in the cloud, tools like Apache Spark or Ray can split your document corpus into chunks and process them in parallel across a cluster. For instance, Spark’s mapPartitions function lets you apply an embedding model to each partition of data. If you’re using Kubernetes, horizontal pod autoscaling can dynamically adjust resources based on demand. For databases, precompute embeddings incrementally as documents are added, rather than reprocessing everything at once. Use asynchronous queues (e.g., RabbitMQ or Amazon SQS) to decouple ingestion from embedding generation, ensuring your system doesn’t get overwhelmed during traffic spikes. Optimize storage by saving embeddings in a binary format like Parquet or HDF5, which are faster to read/write than JSON or CSV.
Finally, optimize the embedding model itself. Smaller models like DistilBERT or all-MiniLM-L6-v2 reduce computation time with minimal accuracy loss. Quantization (e.g., converting 32-bit floats to 16-bit) cuts memory usage and speeds up inference. For frequently used models, cache intermediate outputs (like tokenized text) to avoid redundant work. If you’re using a REST API for embeddings, deploy multiple instances behind a load balancer. Tools like NVIDIA Triton Inference Server help manage high-throughput requests efficiently. Monitor performance with metrics like documents processed per second and adjust batch sizes or hardware as needed. For example, a pipeline using PyTorch with mixed-precision training, Spark for distributed processing, and FAISS for vector storage can handle millions of documents in hours instead of days.