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No—not from the vector count alone. RAM needs depend on vector dimensions, index engine and settings, replicas, and the capacity reserved for indexing and queries. For scale, 100 million 768-dimensional float32 vectors contain about 307.2 GB of raw vector data before the HNSW graph and other index overhead. Quantization, memory-mapped search, and disk-based search can reduce the amount that must reside in RAM, but they have different limits and quality or latency trade-offs.
Why 100 million vectors do not imply a fixed RAM requirement
OpenSearch documentation says default float vectors use 4 bytes per dimension. A first-pass estimate of the uncompressed vector payload is therefore:
vector count × dimensions × 4 bytes
For 100 million 768-dimensional vectors, that is 307.2 billion bytes, or 307.2 GB using decimal units. This is only the vector payload—not a cluster sizing recommendation. HNSW links, index structures, metadata, segments, replicas, and operating-system or JVM headroom add to the resources required.
Consequently, 1.3 TB may be a defensible estimate for a particular workload and deployment, but it is not a requirement implied by “100 million vectors.” Use the selected engine’s sizing method and include room for ingestion, merges, and queries. OpenSearch’s published estimate for a Faiss product-quantized HNSW index illustrates how much other inputs matter:
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1.1 × (((pq_code_size / 8) × pq_m + 24 + 8 × hnsw_m) × num_vectors + num_segments × (2^pq_code_size × 4 × d))
Here, the estimate depends on PQ code size, the number of subvectors, HNSW connectivity, vector dimensions, and segment count as well as vector count. It applies to the documented Faiss PQ estimate; it is not a universal formula for every OpenSearch index.
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Which ways to shrink the footprint fit your workload?
The main choices change different things: quantization reduces vector representation size, memory-optimized search changes how a Faiss index is loaded, and disk-based search trades some in-memory residency for storage access. The figures below describe vector-memory estimates or documented mode behavior, not guaranteed whole-cluster savings.
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|---|---|---|
| Float32 HNSW | Baseline: 4 bytes per dimension for the vector payload, plus graph and index overhead. | Largest vector representation in this comparison; total RAM depends on index and deployment overhead. |
| Lucene scalar quantization | Supports 1-, 2-, 4-, and 7-bit vectors. Ideal vector payload is respectively 3.125%, 6.25%, 12.5%, and 25% of 32-bit storage. | Those percentages do not include HNSW graph and other index costs; lower precision can affect recall. |
| Faiss 16-bit scalar quantization | Approximately half the memory of 32-bit vector data. | Uses the Faiss engine and entails a quality trade-off to validate. |
| Faiss product quantization (PQ) | Stores compact codes, with size controlled by the configured bit budget. | Requires training on representative vectors and is supported with Faiss HNSW or IVF. |
| Faiss memory-optimized search | Memory-maps the index file rather than loading the full index into off-heap memory; relies on the operating-system file cache. | Changes loading, not vector representation; supports Faiss HNSW, not IVF or PQ. |
Disk-based on_disk search |
Uses quantization to keep a smaller representation in memory while full precision can remain on disk. | Storage access affects latency, and behavior depends on OpenSearch version and settings. |
Choose a compression level, then measure quality
Lucene scalar quantization
Lucene scalar quantization is integrated into ingestion and offers 1-, 2-, 4-, and 7-bit choices. The ideal payload ratios provide a useful first comparison with float32, but should not be mistaken for the reduction in total index size: HNSW links and other structures remain. More aggressive compression also makes recall validation particularly important.
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Faiss scalar quantization
Faiss 16-bit scalar quantization is a less aggressive alternative: OpenSearch documentation estimates its vector memory at about 50% of 32-bit storage. Whether the reduced precision is acceptable depends on the corpus and the search quality target.
Faiss product quantization
PQ represents vectors with compact codes whose bit budget can be configured. It can compress more than 16-bit scalar quantization, but introduces a training step: train using representative vectors, then assess the resulting index on the workload. OpenSearch documents PQ for Faiss HNSW or IVF, not as a general engine-independent setting.
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When to keep the index out of RAM
Faiss memory-optimized search
Introduced in OpenSearch 3.1, memory-optimized search lets Faiss HNSW memory-map the index file instead of preloading the entire vector index into off-heap memory. The operating system’s file cache supports access to the mapped data. This is useful when loading the full index into off-heap memory is the constraint; it does not compress vectors and cannot be combined with IVF or PQ.
Disk-based vector search
OpenSearch supports disk-based, or on_disk, vector search with quantization. AWS documentation describes a default on_disk mode using 32× binary quantization and reports 97% lower memory requirements than in-memory mode. It also reports P90 latency of 100–200 ms for that disk-based mode. Treat those figures as AWS-documented reference values, not a performance guarantee for a different cluster, workload, or configuration.
Disk-based search is a candidate when RAM is the binding constraint and the workload can tolerate storage-related latency. Check the deployed OpenSearch version before relying on a particular default or quantization behavior: these are version-sensitive, and the memory-optimized feature is documented as beginning in 3.1.
A practical sizing and validation sequence
- Calculate the raw baseline. Multiply vector count by dimensions by 4 bytes for float32 payload. Record the units used and keep this separate from total index and cluster memory.
- Add the actual index and deployment factors. Account for the selected engine and HNSW parameters, replicas, segment count, ingestion and merge activity, and operational headroom. For Faiss PQ, use the OpenSearch estimate with the parameters for the intended index.
- Choose the least aggressive option that meets capacity. Compare Lucene bit widths or Faiss scalar and product quantization first. If residency is the issue rather than representation size, consider Faiss memory-optimized search; if RAM remains the bottleneck, evaluate disk-based search.
- Benchmark against a quality baseline. Measure recall against exact or higher-precision search, plus p50, p95, and p99 latency, indexing throughput, merge behavior, and failure recovery. Repeat with representative queries and data at the intended scale.
- Verify compatibility and version behavior. Confirm the engine supports the chosen method and check the documentation for the exact deployed OpenSearch version before finalizing settings or assuming defaults.
What you can and cannot conclude before testing
The documented storage ratios and mode constraints can narrow the design choices, but they do not establish a universal recall loss for a particular dimension, dataset, or quantizer. Nor does a vector-only compression ratio predict total RAM savings once graph structures, segments, replicas, and active workload are included. The decisive comparison is measured on the target corpus and latency objective.
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