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Text Embeddings Inference
Text Embeddings Inference is a toolkit for deploying and serving open-source text embeddings and sequence classification models in production environments.
Text Embeddings Inference (TEI) serves as a toolkit for deploying and running open-source text embeddings and sequence classification models. It supports popular model architectures including FlagEmbedding, BERT, GTE, E5, Nomic, and Qwen, enabling efficient extraction for various NLP tasks. The system optimizes inference using technologies such as Flash Attention, Candle, and cuBLASLt to ensure high-performance execution. Users can deploy models via Docker containers for Linux and ARM64 environments or utilize prebuilt binaries for local execution on Apple Silicon via Metal. Production capabilities include distributed tracing with Open Telemetry, Prometheus metrics, and support for both REST and gRPC API interfaces.
Text Embeddings Inference at a glance
Key features
- Token-based dynamic batching
- Flash Attention optimization
- Safetensors and ONNX weight loading support
- Metal acceleration on Apple Silicon
- Distributed tracing with Open Telemetry
- gRPC and REST API interfaces
- Prometheus metrics integration
Use it for
- Extracting text embeddings
- Ranking search results with cross-encoders
- Performing sentiment analysis
- Generating sparse embeddings
Alternatives to Text Embeddings Inference
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- TensorRT — Speeds up inference by 36X compared to CPU-only platforms.
- GLiNER — Enables zero-shot entity extraction without labeled training data on consumer CPUs.
- ZML — It enables Python-free inference while abstracting hardware complexities.
- OpenLLM — It simplifies infrastructure management and optimizes inference speed and cost.
- Flare Local Inference — Optimizes memory and bandwidth for workstations and compact systems.
- LMDeploy — Accelerates inference with high-performance engines and quantization tools.
- ncnn — No runtime dependencies and supports CPU and Vulkan GPU backends.
- OptiLLM — Increases LLM reasoning accuracy by up to 10x without model training.
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