Embedding Compression: 5 Techniques Slash Vector Memory

The guide addresses a real bottleneck: storing millions of high-dimensional embeddings in memory is expensive. The example shows ten million 1,536-dimensional vectors consuming 62 GB in float32, which drops to 15 GB in int8 and 2 GB as packed bits. The actual overhead of an ANN index, metadata, and allocator adds to this.

The five techniques target different parts of the payload. PCA reduces dimensions post-training, while MRL trains the model so prefixes remain usable. Scalar and binary quantization cut bits per dimension, offering 4x and 32x reductions respectively. Product quantization replaces subvectors with centroid IDs.

A key caveat is that compressed representations don't have to produce the final ranking. Retrieving extra candidates and recomputing with higher precision is standard, especially with binary quantization, which loses magnitude information. The tradeoff between memory savings and retrieval quality remains central to production retrieval systems.