Text-Embeddings-Inference

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text-embeddings-inference is an open-source solution for blazing fast inference of text embeddings models. It provides high-performance extraction for popular models like FlagEmbedding, Ember, GTE, and E5, with features like dynamic batching and optimized transformers code.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is text-embeddings-inference?

text-embeddings-inference (TEI) is a toolkit designed for deploying and serving open-source text embeddings and sequence classification models with blazing fast performance. It enables high-performance extraction for a wide range of popular models, including FlagEmbedding, Ember, GTE, and E5. TEI incorporates several advanced features such as token-based dynamic batching, optimized transformer code utilizing Flash Attention, Candle, and cuBLASLt, and support for Safetensors and ONNX weight loading. It also offers production-ready capabilities like distributed tracing with Open Telemetry and Prometheus metrics. The solution supports various model types, including Nomic, BERT, CamemBERT, XLM-RoBERTa, JinaBERT, Mistral, Alibaba GTE, Qwen2, MPNet, ModernBERT, Qwen3, and Gemma3, making it a versatile choice for developers and researchers working with text embeddings.

Best used for

Ideal for AI research engineers who need to deploy and serve text embedding models, backend engineers who need to scale LLM inference, and developers working with high-performance text extraction. Especially valuable for those requiring optimized inference solutions with dynamic batching and production-ready monitoring.

Common actions

deploy text embeddings

serve AI models

optimize model inference

manage text classification

open-sourceworkflowsface swappinggithub copilotlow-code/no-codeautomated workflow"AI Agents"deepfakecollaboration

Capabilities

Key features

Blazing fast inference
Token-based dynamic batching
Optimized transformers code
Safetensors/ONNX weight loading
Distributed tracing (Open Telemetry)
Prometheus metrics
Metal support (Macs)

Target Audience

AI research engineerbackend engineer

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What types of text embedding models does text-embeddings-inference support?

text-embeddings-inference supports a wide range of models including Nomic, BERT, CamemBERT, XLM-RoBERTa, JinaBERT, Mistral, Alibaba GTE, Qwen2, MPNet, ModernBERT, Qwen3, and Gemma3. It also supports various sequence classification and re-ranking models like BAAI/bge-reranker-large.

Can text-embeddings-inference be used with GPUs?

Yes, text-embeddings-inference can leverage GPUs for accelerated inference. To use GPUs, you need to install the NVIDIA Container Toolkit, and your NVIDIA drivers must be compatible with CUDA version 12.2 or higher. Docker images are available for CUDA support.

What performance optimization features does text-embeddings-inference offer?

The toolkit includes token-based dynamic batching, optimized transformers code using Flash Attention, Candle, and cuBLASLt, and support for Safetensors and ONNX weight loading. These features contribute to its blazing fast inference capabilities and efficient resource utilization.

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Research & Education › Academic Research Coding & Development › Backend & APIs Coding & Development › Open Source & Models

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