Infino Blog: engineering notes and case studies
ls blog/engineering/
- Infino: fastest VectorDB at a million, still cheaper at a billion Fastest on VectorDBBench at 1M and 10M — then the same engine reshapes from an in-RAM graph to an object-storage index, roughly 10x cheaper at a billion.
- Fixed grids on vector search - comparing 3 approaches to quantization: FAISS, Turbovec, and Infino Three 4-bit indexes at the same 768 bytes per vector: recall within two points, latency 30× apart.
- What is hybrid search? Two retrievers that miss different things, and the rank fusion that merges their lists.
- Why object-storage vectors use OPANN + Sq16, while RAM uses HNSW One durable vector corpus, with a graph in RAM and bounded range reads on object storage.
- Infino's self-driving vector engine You write a table; the engine inverts it in the background and stamps the ANN dials from your data — 0.99 recall@10 on Cohere-10M, zero knobs.
- Inside a Parquet superfile Why the indexes live inside Parquet, how the footer addresses them, and where the format still hurts.
- Cutting a ten-term BM25 OR from 74.6 ms to 20.0 ms Common terms defeated MaxScore skipping; a 4096-document window removed the per-document cursor merge.
- The 3× to 10× regression from running decode on tokio’s I/O pool Cold vector search went from 1.1 s to 3.7-11 s when CPU-heavy decode starved object-store I/O.
- The three costs of a cold query on S3 File discovery, connection setup, and cache residency on the first query from a new worker.
- A commit on S3 is one compare-and-swap Stage immutable files in parallel; publish the complete table with one conditional pointer write.