77× less vector RAM. 94.5% of baseline retrieval quality. We got early access to Google EmbeddingGemma 2 and tested how far its Matryoshka-compatible embeddings could be compressed in Qdrant. With shorter vectors + quantization + rescoring, we reduced the vector memory footprint by ~77× while retaining 94.5% of baseline retrieval quality. Google also featured our work in their EmbeddingGemma 2 announcement. Qdrant deep dive: qdrant.tech/blog/embeddingge… Google’s announcement: blog.google/innovation-and-a…