---
title: Optimized Embedding Model using Optimum-Intel
 | Developer Documentation
---

LlamaIndex has support for loading quantized embedding models for Intel, using the [Optimum-Intel library](https://huggingface.co/docs/optimum/main/en/intel/index).

Optimized models are smaller and faster, with minimal accuracy loss, see the [documentation](https://huggingface.co/docs/optimum/main/en/intel/optimization_inc) and an [optimization guide](https://huggingface.co/docs/optimum/main/en/intel/optimization_inc) using the IntelLabs/fastRAG library.

Optimization is based on math instructions in the Xeon® 4th generation or newer processors.

In order to be able to load and use the quantized models, install the required dependency `pip install optimum[exporters] optimum-intel neural-compressor intel_extension_for_pytorch`.

Loading is done using the class `IntelEmbedding`; usage is similar to any HuggingFace local embedding model; See example:

```
%pip install llama-index-embeddings-huggingface-optimum-intel
```

```
from llama_index.embeddings.huggingface_optimum_intel import IntelEmbedding


embed_model = IntelEmbedding("Intel/bge-small-en-v1.5-rag-int8-static")
```

```
embeddings = embed_model.get_text_embedding("Hello World!")
print(len(embeddings))
print(embeddings[:5])
```

```
384
[-0.0032782123889774084, -0.013396517373621464, 0.037944991141557693, -0.04642259329557419, 0.027709005400538445]
```