Sub-3 millisecond named entity recognition (NER) inference

Baseten Embeddings Inference (BEI) aims to enhance the speed of encoder inference, particularly for named entity recognition (NER) tasks, by achieving sub-3 millisecond client-side and 1 millisecond server-side inference times in realistic production settings. This is accomplished through various optimizations, including using a Rust-based web server to minimize overhead and employing HTTP/2 connection pooling to reduce latency. Unlike traditional embedding models, NER models provide token-level predictions, requiring unique approaches to minimize latency, such as optimizing the serving stack layers, configuring Nginx to avoid bottlenecks, and co-locating NER pipelines within the same datacenter to reduce network delays. BEI's efficient implementation is significantly faster than traditional PyTorch models, offering ~7.7x speed improvements, and supports deploying BERT-family NER models with ease, allowing existing NER pipelines to upgrade without altering application logic.

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