Quantizing 10M EmbeddingGemma vectors to 1 bit with MRL cuts RAM from 30GB to 0.4GB

What happened and what to do

The author reports that storing 10 million EmbeddingGemma 2 vectors with 1-bit TurboQuant quantization and MRL reduction to 256 dimensions reduced RAM usage from about 30GB to 0.4GB. The stated quality loss is about 5%, with 94.5% quality retained when rescoring is applied. The case shows a concrete memory-versus-quality trade-off in embedding quantization for retrieval systems.

For a company with large document collections, this trade may make semantic search possible on a single server, without relying on machines with very large memory. In practice, it is worth assessing the loss acceptable for the use case, measuring retrieval quality on a set of real queries, and deciding whether rescoring with full vectors justifies its added latency. This assessment should be run on the company's own data, not only on the reported benchmark.

How the consultancy can help

Diagnosis of the current vector index, measurement of memory and retrieval quality on real queries, and a proposed configuration with quantization, dimension reduction and rescoring. Implementation in the company's own environment, with latency and quality monitoring over time.

Next step

Send a short description of your collection, vector volume, current infrastructure and expected search quality. Wendelmaques will reply with a scoped proposal to evaluate and implement compression for your index.