Quantization techniques for multimodal embeddings aim to reduce memory usage and computational costs while preserving the quality of combined text, image, or audio representations. Three effective methods include scalar quantization, product quantization, and binary quantization. Each balances trade-offs between compression ratio, inference speed, and accuracy, making them suitable for different scenarios in multimodal applications.
Scalar quantization reduces the precision of embedding values (e.g., from 32-bit floats to 8-bit integers) uniformly across all dimensions. This is straightforward to implement and works well when embeddings have a uniform distribution of values. For example, CLIP embeddings (which align text and images) can often tolerate 8-bit quantization with minimal accuracy loss, as shown in benchmarks like the Flickr30K retrieval task. Libraries like PyTorch’s quantize module simplify this process by scaling and rounding values. However, scalar quantization struggles with embeddings that have outliers or highly skewed distributions, as compressing extreme values can distort similarity scores.
Product quantization (PQ) divides high-dimensional embeddings into subvectors and quantizes each separately, achieving higher compression ratios. This is particularly useful for multimodal systems that require efficient nearest-neighbor search, such as recommendation engines combining user text queries and product images. For instance, Facebook’s FAISS library uses PQ to compress billion-scale multimodal datasets, enabling real-time retrieval. By training separate codebooks for each subvector, PQ preserves more nuanced relationships between modalities than scalar methods. However, PQ adds complexity during training and inference, as it requires maintaining codebooks and reconstructing vectors during searches.
Binary quantization (e.g., binarizing embeddings to 0/1 values) offers extreme compression and ultra-fast similarity computation via bitwise operations. This works best for applications prioritizing speed and memory savings over exact accuracy, such as on-device multimodal search in mobile apps. For example, binary versions of OpenAI’s CLIP embeddings can reduce memory usage by 32x while retaining ~80% of retrieval accuracy. However, binary methods risk significant information loss, especially for embeddings capturing subtle cross-modal relationships. Hybrid approaches, like using binary codes for coarse retrieval followed by higher-precision reranking, can mitigate this.
When choosing a technique, consider the use case: scalar quantization suits balanced value distributions, PQ excels in high-compression retrieval systems, and binary methods prioritize speed. Tools like TensorFlow Lite (for scalar) and FAISS (for PQ) provide off-the-shelf implementations. Testing accuracy after quantization using domain-specific benchmarks (e.g., cross-modal retrieval metrics) is critical to validate performance trade-offs.