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In Infino’s described architecture, the index follows where the working set lives: HNSW serves vectors that fit in resident RAM, while OPANN + Sq16 serves vectors held in object storage. The difference is the shape of the reads. HNSW follows dependent graph links, which are quick memory accesses in RAM but can become serial remote requests on a cold object store. OPANN first routes a query to likely regions, then fetches bounded, contiguous ranges that can be requested together. This is an explanation of Infino’s design, not a rule that every vector database uses.
Why the storage location changes the index choice
Vector search needs to find close matches without scoring every vector. HNSW does this by navigating a graph: a query starts at an upper layer and follows promising neighbors toward a shortlist at the bottom. Each next hop depends on the node found at the previous hop.
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When the graph and vectors are resident in RAM, those dependent hops are memory accesses. When the same walk touches cold object storage, each read can determine which address must be requested next. That dependency makes it difficult to issue reads concurrently, so remote latency can accumulate across the traversal. Loading the full graph locally can turn the problem into a bulk load, but requires enough local capacity to hold it.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsInfino’s author Ashish Mishra summarizes the distinction: “HNSW is an excellent in-memory vector index. It is also a poor object-storage access pattern.” Infino’s August 20, 2026 article presents this as the rationale for its own serving paths, not a universal verdict on HNSW.
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How Infino’s OPANN path changes remote reads
Route before fetching
Instead of walking an unpredictable chain of graph links, OPANN uses a routing structure to select likely vector regions first. It then fetches bounded, contiguous cluster ranges, scores their compact vectors, and reranks a shortlist.
Make the I/O batchable
Because the likely ranges are selected before their contents arrive, the reads can be issued concurrently, coalesced, and cached. The intended advantage is not that object storage becomes RAM; it is that the remote access pattern is planned rather than a succession of dependent hops.
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Keep result data aligned
Infino describes vectors and the index in a Parquet superfile, with returned IDs resolving against the same snapshot as scalar filters and BM25 results. That is an implementation detail of this architecture, relevant when evaluating whether vector matches and associated row data stay consistent.
What Sq16 changes—and what it does not
Sq16 is Infino’s compact vector representation: the article says the system accepts fp32 input and internally represents each dimension with 16 bits. For a 1024-dimensional vector, the article estimates 4 KiB of raw fp32 values versus about 2 KiB with Sq16. At ten million vectors, that is about 40 GiB versus 20 GiB of raw vector values, before graph links, metadata, and other overhead.
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Smaller vectors can reduce both resident memory needs and bytes transferred from object storage. They do not eliminate the graph or metadata costs in an HNSW path, and they do not guarantee unchanged recall. OpenSearch’s documentation similarly explains that 16-bit vectors can use half the memory of 32-bit vectors in the best case, while quantization choices and vector distributions affect recall. Its documentation describes OpenSearch mechanisms, not Infino’s OPANN internals. OpenSearch vector search documentation
What Infino’s reported measurements show
Infino reports a Cohere run of 9.4 million vectors at 768 dimensions with top-10 retrieval on its post-drain OPANN + Sq16 path. The vendor-reported figures are recall@10 of 0.995, warm p50 latency of 9.37 ms, and warm p99 of 10.25 ms. They are not an independently reproduced or controlled comparison with HNSW.
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The same article reports a cold-open median of 661 ms; its first cold query took 4.60 seconds and fetched 2.55 GiB of index ranges. Those cold-start numbers matter: warm latency alone does not describe the cost of opening a cold index or serving its first query. The figures are from Infino’s article published August 20, 2026. Infino’s benchmark and architecture account
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These approaches answer different operational conditions rather than establishing a universal winner. For a real workload, compare the whole serving path, not just steady-state latency:
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- Working-set residency: Can the vectors, graph, and useful cache fit in RAM, including overhead beyond raw vector values?
- Remote I/O shape: Are reads independent and batchable, or does each result determine the next request?
- Cold and warm behavior: Measure open time and first-query latency alongside warm p50 and p99.
- Recall at the target k: Evaluate quantized candidates and any reranking against the quality required by the application.
- Total cost and consistency: Include index construction/loading, graph links, metadata, cache, and whether vector IDs resolve to the right filtered row snapshot.
OpenSearch documents other memory-optimized and on-disk vector-search options, including on-demand loading and two-phase rescoring with full-precision vectors for a shortlist. These illustrate that storage and recall tradeoffs can be implemented in different ways; they do not provide a controlled ranking against Infino’s OPANN. OpenSearch vector search documentation
How this differs from a managed vector-storage service
A separate product category should not be conflated with Infino’s design. AWS describes S3 Vectors as a purpose-built object-storage service for storing and querying vectors, with indexes and metadata filtering. AWS positions it for less-frequent searches, claiming sub-second search for infrequent workloads and as low as 100 ms for more frequent queries. Those are AWS’s product statements; they do not establish that S3 Vectors uses OPANN, Parquet superfiles, or Sq16. AWS S3 Vectors documentation
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