A way to cut existing vector storage by 49%
CyBurn Digital says it reduced storage by 49% for systems. The cost problem came from storing standard 1024-dimensional embeddings in the cloud. Built-in database , such as Pinecone SQ, lowers number precision but does not reduce the actual number of dimensions.
The goal was to nearly halve the vector size without badly hurting accuracy. Matryoshka Representation Learning can do this when a model is trained for it from the start, but that did not fit millions of older vectors already made with standard models such as BGE-M3. Re-embedding all of that data was considered too expensive.
Standard PCA or SVD also failed because cutting the matrix lost useful information in the long tail. The execution code remains closed, but the mathematical design and a live sandbox are available for outside testing.
Key points
- The bottleneck was cloud cost from storing 1024-dimensional embeddings.
- The target was to reduce vector dimensions without a large loss in accuracy.
- Basic lowers precision but does not cut the number of dimensions.
- Re-embedding millions of older vectors was considered too expensive.
- The code is , while the math and live sandbox are shared for testing.