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Not necessarily. One hundred million 768-dimensional float32 vectors require about 307.2 GB of raw vector payload (before graph links, metadata, replicas, segments, and operating-system or JVM headroom). Whether the finished OpenSearch deployment approaches 1.3 TB depends on dimension, HNSW settings, engine, shard and segment layout, replica count, recall target, and latency SLO. Quantization, memory-mapped indexes, or disk-based search can reduce the RAM requirement before the vectors become an index.
Why “100 million vectors” is not a RAM specification
OpenSearch’s default float vectors use 4 bytes per dimension. The first estimate is therefore:
vector_bytes = number_of_vectors × dimensions × 4
For 100,000,000 vectors with 768 dimensions, that is 307,200,000,000 bytes, or 307.2 GB in decimal units (roughly 286 GiB). This is only the payload. An HNSW index adds neighbor links and other structures; metadata, deleted documents, segment copies, replicas, merge working space, the JVM, the operating system and query concurrency all need capacity too. Ingestion and merges can temporarily require more memory than a steady-state search workload.
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A 1.3 TB recommendation may therefore be reasonable for a particular architecture, but it cannot be derived from vector count alone. Calculate the selected engine’s index footprint, then reserve room for operations instead of treating the raw-vector number as a cluster size.
Compression choices before indexing
| Option | Memory behavior | Quality and operational trade-offs |
|---|---|---|
| Float32 HNSW | Highest vector footprint; 4 bytes per dimension | Simplest representation, but the full graph and vector payload may exceed available RAM at 100-million scale |
| Lucene scalar quantization | 1-, 2-, 4-, or 7-bit vector values; ideal vector payload is 3.125%, 6.25%, 12.5%, or 25% of float32 storage, respectively | Integrated during ingestion; graph links and other index overhead remain, and lower precision can reduce recall |
| Faiss 16-bit scalar quantization | Approximately 50% of the 32-bit vector memory according to OpenSearch documentation | Requires the Faiss engine and trades some precision for a moderate reduction |
| Faiss product quantization (PQ) | Stores compact codes whose size is set by the code bit budget and number of subvectors | Requires training on representative vectors; available with Faiss HNSW or IVF, not every engine or index type |
| Faiss memory-optimized search | Memory-maps the index instead of preloading the entire index into off-heap memory | Changes how data is loaded, not how vectors are represented; Faiss HNSW only, with no IVF or PQ |
Disk-based (on_disk) search |
Keeps compressed data in RAM and can retain full-precision vectors on disk | Reduces RAM pressure but introduces storage latency and version-specific behavior |
Lucene scalar quantization: the simplest shrink step
Lucene scalar quantization is available at ingestion and supports 1, 2, 4, and 7 bits. The ideal payload ratios above describe vector values only; HNSW links, document metadata, segment structures and replicas do not shrink by the same percentages. Use those ratios for a first estimate, not as a promise for whole-index memory.
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Start with the least aggressive bit depth that meets your recall target. Build a test index with the production dimension and HNSW parameters, then compare nearest-neighbor results with a float32 baseline. If recall is insufficient, move to a larger bit budget before changing the graph or shard layout.
Faiss options: moderate reduction or deeper compression
16-bit scalar quantization
Faiss 16-bit vectors use about half the memory of 32-bit vectors, according to OpenSearch documentation. This is a useful intermediate point when 1- to 7-bit Lucene quantization is too damaging to quality or when you want to avoid a training pipeline.
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Product quantization
Product quantization splits each vector into subvectors and replaces each subvector with a compact code. It can compress more aggressively than 16-bit storage, but a representative training set is required before indexing. PQ is supported with the Faiss engine and Faiss HNSW or IVF, so confirm that your chosen index type and search features are compatible.
For a Faiss PQ HNSW estimate, OpenSearch publishes this expression:
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- VERIFIED FITMENT — Compatible with EPYC Genoa, Threadripper PRO, TRX50, WRX90, Xeon W-2500. Spec-matched to your board's memory-population rules.
- ENTERPRISE STABILITY — On-module ECC catches and corrects single-bit errors on the fly — stopping silent data corruption and crashes before they reach your work — on a standard unbuffered DIMM that drops into ECC-capable workstation and entry-server boards.
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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)) bytes
Here, d is the vector dimension; pq_m, pq_code_size, hnsw_m, the vector count and segment count all affect the result. The segment term explains why two indexes with the same number of vectors can have different footprints.
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- ECC Registered RDIMM; 2Rx4 (EC8, 10x4) - Dual Rank x4; JEDEC DDR5 standard 1.1V
- Improves system performance, workload capacity, and reduces bottlenecks by increasing memory (RAM) resources
- Note: EC8 (10x4) ECC Registered modules cannot be mixed with EC4 (9x4) ECC Registered modules or with different ECC types such as ECC Unbuffered, ECC Load Reduced or Non-ECC Unbuffered; (Memory compatibility can vary among different system models and their installed components; please verify compatibility and follow memory channel guidelines to ensure maximum performance)
When the index does not have to fit entirely in RAM
Memory-optimized Faiss HNSW
OpenSearch describes memory-optimized search as a way for Faiss to run without loading the entire vector index into off-heap memory. The index is memory-mapped and the operating system’s file cache supplies pages as needed. This reduces the amount that must be preloaded, but it does not quantize the vectors. It is limited to Faiss HNSW and cannot be combined with IVF or PQ. OpenSearch documentation identifies this capability as introduced in version 3.1; verify the exact support in your deployed release.
Disk-based or on_disk search
Disk-based vector search combines quantization with storage access. AWS documentation describes the default on_disk mode as using 32× binary quantization and reports 97% lower memory requirements than in-memory mode, with P90 latency of 100–200 ms. Those figures are AWS’s documented reference for that mode, not a guarantee for every disk, corpus or cluster. Storage performance, cache warmth, concurrent queries and shard placement can change latency substantially.
Because full-precision values can remain on disk while compressed representations serve the search path, this approach is the clearest escape hatch when RAM is the limiting resource and a higher latency SLO is acceptable.
A sizing and configuration sequence
- Record the workload. Fix the dimension, vector count, distance metric, target recall, p50/p95/p99 latency limits, ingestion rate, replica count and shard plan. These inputs determine the index, not the count alone.
- Calculate the float32 baseline. Multiply vectors by dimensions by four, then add graph, metadata, segment, replica and operational headroom. Keep this baseline for comparison even if you will quantize.
- Choose the least damaging quantizer. Test Lucene scalar bit depths first when using the Lucene engine. Test Faiss 16-bit SQ for a roughly half-size representation, or PQ when deeper compression justifies training and Faiss-specific constraints.
- Choose a residency model. If the full index must be hot in RAM, size for the quantized graph and its overhead. If preload memory is the problem, evaluate Faiss memory mapping. If RAM remains the bottleneck, evaluate
on_disksearch and its storage-latency impact. - Build a production-shaped pilot. Use representative vectors, the intended shard and segment behavior, production-like replicas and the same HNSW parameters. Avoid extrapolating from a tiny, unmerged test index.
- Measure quality and operations. Compare recall against an exact or high-precision baseline, and record p50, p95 and p99 latency, indexing throughput, merge behavior, cache effects and failure recovery. No universal recall-loss percentage applies to every corpus or quantizer.
- Recheck release behavior. Quantization defaults and
on_diskbehavior are version-sensitive. Confirm the settings and engine constraints against the documentation for the OpenSearch version actually running in the cluster.
How to interpret the result
- If float32 HNSW plus replicas and headroom fits comfortably, keep the simpler representation and avoid unnecessary quality risk.
- If memory is tight but recall is sensitive, try 16-bit scalar quantization or a higher-bit Lucene setting before using PQ or binary quantization.
- If the index is too large to preload, memory mapping can reduce off-heap pressure without changing vector precision, provided Faiss HNSW is acceptable.
- If RAM is the hard constraint and a roughly 100–200 ms P90 reference is compatible with your service, test disk-based search with production storage rather than adding RAM blindly.
Bottom line for 100 million vectors
There is no fixed “1.3 TB per 100 million vectors” rule. A 768-dimensional float32 payload starts at 307.2 GB, and the final requirement emerges from HNSW links, engine and quantizer, segments, replicas, operating-system and JVM needs, and the workload’s latency and recall targets. Quantize before indexing when quality permits; use Faiss memory mapping when preload memory is the issue; and use version-appropriate disk-based search when keeping the entire index in RAM is the wrong constraint.
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