LightOn Unveils FastPlaid Architecture for Late-Interaction Models

• Michael Willson
• June 13, 2025

LightOn has just announced FastPlaid, a new architecture that promises to speed up late-interaction models like ColBERT by over 500%. If you’re wondering how this works, here’s the simple answer: FastPlaid uses a smarter design to process search queries faster, without sacrificing accuracy. In this article, we’ll break down what FastPlaid is, why it matters, and how it stacks up against other technologies.

What Is FastPlaid?

FastPlaid is LightOn’s latest contribution to the field of information retrieval. It’s designed to improve the performance of late-interaction models. Late-interaction models, like ColBERT, process text in two steps: they create embeddings first and then match them using more precise scoring. This approach balances speed and quality but can still be slow, especially on large datasets.

FastPlaid changes the game by combining the benefits of PLAID—originally designed for faster GPU and CPU search—with transformer-based models like ModernColBERT. It’s available through LightOn’s PyLate library, making it easy for developers to test and experiment with less than 80 lines of code.

How FastPlaid Works

The FastPlaid architecture improves efficiency by rethinking how transformers handle embeddings and token interactions. Instead of treating every pair of tokens separately, FastPlaid uses smart grouping and compression to cut down on redundant work. This boosts throughput while maintaining high retrieval quality, making it ideal for large-scale search engines and question-answering systems.

Key Advantages of FastPlaid

FastPlaid offers several key advantages over other retrieval models:

• High Speed: FastPlaid is up to 554% faster than traditional ColBERT models on GPU and CPU. 
• Great Accuracy: It keeps the same high-quality retrieval results that users expect from ColBERT-style models. 
• Easy to Use: Developers can integrate FastPlaid into existing systems using LightOn’s PyLate library, with minimal code changes. 
• Supports ModernBERT: It works with ModernBERT retrieval models, giving developers more flexibility. 

How FastPlaid Compares to Other Models

FastPlaid isn’t the only technology trying to improve search and retrieval. Let’s see how it stacks up against other popular models.

FastPlaid vs Other Late-Interaction Models

Model	Speed Improvement	Retrieval Quality	Ease of Use	Limitations
FastPlaid (LightOn)	Up to 554% faster	State-of-the-art	Easy with PyLate	Research-stage; needs broader benchmarks
PLAID on ColBERTv2	7× GPU, 45× CPU	Maintains quality	Optimized engine	No transformer integration
Dense Bi-encoders	High throughput	Lower accuracy	Widely supported	Less precise for complex queries
Cross-encoder BERT	Very slow	High accuracy	Tooling available	Not scalable for large datasets

This table shows that FastPlaid is leading in speed without giving up accuracy—something few competitors can claim.

Real-World Use Cases

FastPlaid is especially useful for applications like search engines, question-answering systems, and knowledge retrieval. Because it can handle large datasets quickly, it’s a great fit for industries that rely on fast, accurate information access, like legal research, finance, and customer support.

Key Features of FastPlaid

FastPlaid offers features that make it both powerful and practical:

• Transformer Integration: Works with ModernBERT retrieval models. 
• Flexible Deployment: Compatible with vector databases like Qdrant, LanceDB, Weaviate, and Vespa. 
• Open Source: Available through PyLate, encouraging experimentation and customization. 
• Compact Implementation: Requires fewer than 80 lines of code to set up. 

Key Features of FastPlaid

Feature	Benefits
Transformer Support	Uses ModernBERT and other transformer models
Speed Improvements	554% faster on GPU and CPU for late-interaction tasks
High Accuracy	Maintains SOTA retrieval quality
Easy Integration	PyLate library; less than 80 lines of code required
Compatibility	Works with popular vector databases
Open Source	Encourages adoption and customization

This table highlights why FastPlaid is a strong choice for developers looking to build high-performance search systems.

Where FastPlaid Needs More Work

While FastPlaid is impressive, it’s still early days. Here are some areas where more development and testing are needed:

• Benchmark Coverage: While initial results look great, more public benchmarks would help validate FastPlaid’s performance across different industries. 
• Integration: Currently focused on ColBERT-style models; expanding support for other architectures would help adoption. 
• Real-World Testing: Developers need case studies and real-world examples to understand how FastPlaid performs in production environments. 

Why FastPlaid Matters for Developers

FastPlaid’s combination of speed and accuracy makes it a game-changer for search and retrieval systems. It’s especially useful for anyone building applications that need to balance high quality with fast response times. For those interested in applying AI and search technologies in business, adding a Marketing and Business Certification or a Data Science Certification can help you make the most of these tools. Additionally, earning an AI Certification can deepen your knowledge of how architectures like FastPlaid work and how to optimize them for your specific needs.

Conclusion

LightOn’s FastPlaid architecture is a big step forward in late-interaction search models. By combining speed, accuracy, and ease of use, it gives developers a new way to build high-performance applications. While there’s still work to be done in benchmarking and real-world testing, FastPlaid shows great promise. With the right certifications and skills, developers can unlock the full potential of this new technology.