Shrinking Embeddings for Speed and Accuracy in AI Models

As AI continues to evolve, so does the need for faster, more efficient systems. Two key innovations — Matryoshka Representation Learning (MRL) and Binary Quantization Learning (BQL) — are setting new standards for how we handle embeddings, the core of AI data representations.

Traditional embeddings, though powerful, face serious bottlenecks in memory, speed and cost, especially as data sets scale. MRL and BQL solve this by shrinking embeddings while maintaining accuracy, drastically improving efficiency. Let’s break down how these techniques work and why they matter.

What are AI-Powered Embeddings?

In AI, embeddings are the result of a model’s inference process, which takes an input object like text, an image or any structured data and translates its features into a vector — a dense, fixed-length representation in a high-dimensional space or even multiple vectors. These number lists, or vectors, capture the meaning or features of the data in a way that computers can better understand and work with.

Real-world applications like search engines, recommendation systems and natural language processing tools rely on vast amounts of data or search through billions of records. All of this data may have vector representation, making efficient handling crucial for maintaining performance and scalability.

Vectors often contain thousands of dimensions. While this captures rich detail, it leads to significant drawbacks when scaled:

• Memory hogs. Large embeddings demand massive storage. Each number in an embedding is typically stored as a 32-bit float, meaning a 1,024-dimensional embedding consumes 4KB of memory. This quickly adds up to enormous memory requirements when dealing with millions or billions of embeddings.
• Slow to process. More dimensions mean slower processing and more computations. Comparing embeddings, which is essential for tasks like search and recommendations, requires complex mathematical operations on these large vectors. The more dimensions an embedding has, the more calcula tions are needed, increasing computational costs and energy consumption. Additionally, as data sets grow, processing and comparing these large embeddings becomes a bottleneck. This can lead to slower application response times and limit the scalability of AI systems.
• Costly to store. Big data sets push up storage and bandwidth costs. Storing large embeddings for millions of items can be expensive, especially when using fast storage solutions needed for quick retrieval.
• Energy drain. Processing larger embeddings consumes more energy, increasing operational costs and environmental implications.

Additionally, a new wave of late-interaction models produces arrays of vectors for a single document, pushing requirements for even further computation and storage optimizations.

These challenges make AI systems sluggish, expensive and less scalable, problems that MRL and BQL aim to fix. Let’s explore each approach in more detail.

MRL for Efficient Search

Matryoshka Representation Learning (MRL) is a clever approach to creating flexible, multisized embeddings. Named after Russian nesting dolls, MRL creates embeddings with a hierarchy of sizes. Here’s how it works:

• Hierarchical structure: Smaller embeddings are nested within larger ones. For example, a 1,024-dimensional embedding might contain a 512-dimensional embedding, a 256-dimensional embedding, and so on.
• Ordered importance: The dimensions are ordered by importance. The first dimensions capture the most crucial information, with each subsequent dimension adding more nuanced details.
• Flexible use: Choose the size you need —128 dimensions for quick searches, 1,024 for detailed analysis. You can use a subset of the dimensions. depending on the task or computational resources available. For simple classification tasks or fast, coarse-level searches, where candidates are reranked with the most significant dimensionality version, you might use only the first 64 or 128 dimensions.

MRL offers both adaptability and efficiency. It allows the same embedding to be used for quick, approximate searches and detailed comparisons. By starting with smaller embeddings for initial filtering and scaling up only when needed, MRL reduces computational load. Plus, it’s applied as a post-processing step, meaning flexible embeddings can be generated without adding extra inference costs with the AI model.

For example, an e-commerce platform can use MRL to make its search process more efficient. For quick searches, it would initially use a smaller, 128-dimensional embedding to find potential product matches faster. Once the top results are identified, the platform can refine the rankings using a larger, 1,024-dimensional embedding, ensuring a balance between speed and accuracy. This approach helps optimize performance without sacrificing quality.

BQL for Reduced Complexity

Binary Quantization Learning takes a different approach, drastically reducing embeddings’ memory footprint and computational complexity. Here’s how it works:

1. Binary representation instead of floating-point. BQL turns 32-bit data into simple zeros and ones, shrinking data dramatically.
2. Learned quantization. During training, the model learns to convert complex data into binary form while retaining key information. It effectively maps high-dimensional, floating-point data into a binary space, preserving as much relevant detail as possible for accurate results.
3. Compact storage. The resulting binary vectors can be stored efficiently. For example, a 1,024-dimensional binary vector only needs 128 bytes of storage, compared to 4,096 bytes for a floating-point equivalent.

BQL dramatically improves AI efficiency by offering massive storage savings, faster computations and reduced bandwidth requirements. By compressing data up to 32 times and accelerating processing, BQL enables AI systems to easily manage large-scale tasks, making it an essential tool for scalable, high-performance applications.

For example, a large-scale recommendation system, like those used in e-commerce or streaming platforms, can use BQL to efficiently represent both user preferences and product/item characteristics. By using binary embeddings, the system can store and process data for millions of users and items with minimal storage and computational costs. This efficiency allows the system to deliver real-time recommendations, even with massive amounts of data, while keeping operational costs low.

Benefits of Both Worlds (MRL and BQL)

Combining MRL and BQL creates a powerful synergy that takes AI efficiency to the next level. With hierarchical binary embeddings, we can generate embeddings in varying sizes ( 64, 128, 256, 512 bits), allowing for flexible precision. Smaller embeddings work for tasks needing less accuracy, while larger ones provide more detail when necessary. This approach offers extreme efficiency, blending the space-saving and computational benefits of binary representations with the adaptability of multisized embeddings, making it ideal for scalable AI systems.

In real-world examples, this combination can lead to remarkable improvements:

• Storage reduction. Embeddings can use up to 64 times less space than full-precision floating-point embeddings.
• Faster search. Similarity searches can be up to 20 times faster, enabling real-time responsiveness even with massive data sets.
• Cost savings. Lower computational and storage needs translate to significant reductions in infrastructure costs.
• New applications. These efficiency gains make it possible to deploy advanced AI on resource-constrained devices or scale existing applications to much larger data sets.

By addressing the limitations of traditional embeddings, MRL and BQL are paving the way for more efficient, scalable, and accessible AI systems across a wide range of applications.

Looking Ahead

MRL and BQL aren’t just incremental improvements—they’re game-changers. By enabling more efficient storage, faster processing and flexible AI applications, these techniques unlock new possibilities, making once-impractical innovations a reality.

The real-world benefits are profound: faster search engines, more responsive recommendation systems, cost-effective AI applications, and a reduced carbon footprint, thanks to lower energy and hardware requirements. These breakthroughs showcase the power of creative problem-solving to overcome technological limits. As AI advances, these innovations will pave the way for creating more efficient and accessible systems that benefit everyone.

Vespa is a platform for developing and running real-time AI-driven applications for search, recommendation, personalization and retrieval-augmented generation (RAG).

Vespa supports both MRL and BQL by enabling highly efficient storage and processing of embeddings, which are crucial for AI applications that deal with large data sets. With Vespa, you can query, organize, and make inferences in vectors, tensors, text and structured data. Vespa can scale to billions of constantly changing data items and thousands of queries per second, with latencies below 100 milliseconds.

It’s available as a managed service and open source. Learn more about Vespa here.