# 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.