# FastEmbed

> A lightweight, fast Python library for embedding generation using ONNX Runtime that achieves 12x inference speedup on CPUs, requires no GPU, and provides state-of-the-art accuracy with Flag Embedding as the default model, maintained by Qdrant.

- **URL:** https://github.com/qdrant/fastembed
- **Category:** SDKs & Libraries
- **Tags:** embedding-inference, onnx, Lightweight
- **Updated:** 2026-03-20 13:25
- **Canonical page:** https://vectordb.ever.works/ru/items/fastembed

## Details

## Overview

FastEmbed is a lightweight, fast library for embedding generation built and maintained by Qdrant. It uses ONNX Runtime instead of PyTorch, making it ideal for CPU-only environments and serverless deployments.

## Key Features

### Lightweight Architecture
- Minimal external dependencies
- No GPU required
- Doesn't download GBs of PyTorch dependencies
- Uses ONNX Runtime for efficient inference
- Perfect for serverless runtimes (AWS Lambda, etc.)

### Performance
- **12x inference speedup** on CPUs via ONNX optimization
- Faster than PyTorch-based implementations
- Quantized models for CPU (and Mac Metal)
- Optimized for edge computing
- Best compute efficiency

### Accuracy
- Better than OpenAI Ada-002
- Default model: Flag Embedding (MTEB leaderboard leader)
- State-of-the-art results on benchmarks
- Multiple model options available

## Supported Embeddings

1. **Text Embeddings**: Traditional text-to-vector embeddings
2. **Image Embeddings**: Visual similarity search
3. **Sparse Embeddings**: SPLADE-based sparse vectors
4. **Reranking**: Cross-encoder models for reranking

## Multi-Language Support

Available in:
- **Python**: `pip install fastembed`
- **Rust**: Available as crate on crates.io
- **Go**: Native Go implementation
- **JavaScript**: Node.js support

## Use Cases

### Serverless Deployments
- AWS Lambda functions
- Google Cloud Functions
- Azure Functions
- Edge runtime compatibility

### Edge Computing
- On-device inference
- IoT applications
- Mobile deployments
- Q1 2026 target: 1M device deployments

### Resource-Constrained Environments
- CPU-only servers
- Development laptops
- CI/CD pipelines
- Cost-optimized cloud instances

## Integration

### Qdrant Integration
Native integration with Qdrant vector database:
```python
from fastembed import TextEmbedding
from qdrant_client import QdrantClient

embedding = TextEmbedding()
client = QdrantClient(":memory:")
vectors = list(embedding.embed(["Hello world"]))
```

### Framework Support
- Haystack integration
- LangChain compatibility
- Direct API usage
- Custom pipeline integration

## Model Selection

### Default Model
- Flag Embedding (BAAI)
- MTEB leaderboard leader
- Good balance of speed and accuracy

### Available Models
- Various sizes (small, base, large)
- Multilingual options
- Specialized domain models
- Custom model support

## Technical Advantages

### ONNX Runtime Benefits
- Cross-platform compatibility
- Hardware acceleration (CPU, GPU, Mac Metal)
- Smaller memory footprint
- Faster inference
- Production-ready

### Quantization Support
- INT8 quantization for CPU
- Metal quantization for Mac
- Minimal accuracy loss
- Significant speed improvements

## Deployment Scenarios

### Cloud-Native
- Kubernetes deployments
- Container-friendly
- Horizontal scaling
- Resource-efficient

### Hybrid and Edge
- On-premise deployments
- Edge data centers
- Fog computing nodes
- IoT gateways

## Performance Benchmarks

### CPU Inference
- 12x faster than PyTorch equivalent
- Sub-millisecond latency for small texts
- Efficient batch processing
- Linear scaling with text length

### Memory Efficiency
- Low RAM requirements
- Small model files
- Efficient caching
- Minimal overhead

## Production Readiness

### Battle-Tested
- Used in Qdrant Cloud
- Powers production workloads
- Regular updates and maintenance
- Active community support

### Enterprise Features
- Stable API
- Comprehensive documentation
- Type hints and IDE support
- Unit and integration tests

## Recent Developments (2025-2026)

- November 2025: ONNX optimization analysis
- Q1 2026: Target deployment on 1M devices
- Ongoing model additions
- Performance improvements

## Comparison to Alternatives

### vs. PyTorch-based Libraries
- 12x faster on CPU
- Smaller dependencies
- Lower memory usage
- No GPU required

### vs. API-based Solutions
- No API latency
- No usage costs
- Data privacy (local inference)
- Offline capability

## Getting Started

```python
# Install
pip install fastembed

# Basic usage
from fastembed import TextEmbedding

model = TextEmbedding()
embeddings = list(model.embed(["Hello world"]))
```

## Pricing

Completely free and open-source. No API costs, no usage limits.

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