# Text Embeddings Inference Text Embeddings Inference (TEI) is a comprehensive toolkit designed for efficient deployment and serving of open source text embeddings models. It enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE, and E5. TEI offers multiple features tailored to optimize the deployment process and enhance overall performance. **Key Features:** * **Streamlined Deployment:** TEI eliminates the need for a model graph compilation step for an easier deployment process. * **Efficient Resource Utilization:** Benefit from small Docker images and rapid boot times, allowing for true serverless capabilities. * **Dynamic Batching:** TEI incorporates token-based dynamic batching thus optimizing resource utilization during inference. * **Optimized Inference:** TEI leverages [Flash Attention](https://github.com/HazyResearch/flash-attention), [Candle](https://github.com/huggingface/candle), and [cuBLASLt](https://docs.nvidia.com/cuda/cublas/#using-the-cublaslt-api) by using optimized transformers code for inference. * **Safetensors weight loading:** TEI loads [Safetensors](https://github.com/huggingface/safetensors) weights for faster boot times. * **Production-Ready:** TEI supports distributed tracing through Open Telemetry and exports Prometheus metrics. **Benchmarks** Benchmark for [BAAI/bge-base-en-v1.5](https://hf.co/BAAI/bge-large-en-v1.5) on an NVIDIA A10 with a sequence length of 512 tokens:

**Getting Started:** To start using TEI, check the [Quick Tour](quick_tour) guide.