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At float32, 100 million embeddings need about 143 GB of raw vector storage at 384 dimensions, 572 GB at 1,536 dimensions, or 1.14 TB at 3,072 dimensions. Those figures cover the vectors alone—not the complete RAM requirement for a vector database. The actual amount depends on the datatype, index, metadata, replication, storage tiers, and workload.
Raw RAM for 100 million embeddings
For a single vector field stored fully in memory, calculate raw vector bytes as count × dimensions × bytes per dimension. Float32 uses four bytes per dimension, so 100 million vectors require 100,000,000 × dimensions × 4 bytes. The estimates below use decimal gigabytes (GB), as in Hugging Face’s published table; binary gibibytes (GiB) are smaller numerically for the same byte count.
| Dimensions | Float32 raw vector storage | Example models listed by Hugging Face |
|---|---|---|
| 384 | 143.05 GB | all-MiniLM-L6-v2; bge-small-en-v1.5 |
| 768 | 286.10 GB | all-mpnet-base-v2; bge-base-en-v1.5; jina-embeddings-v2-base-en; nomic-embed-text-v1 |
| 1,024 | 381.46 GB | bge-large-en-v1.5; mxbai-embed-large-v1; Cohere embed-english-v3.0 |
| 1,536 | 572.20 GB | OpenAI text-embedding-3-small |
| 3,072 | 1,144.40 GB | OpenAI text-embedding-3-large |
These are Hugging Face’s estimates for 100 million float32 embeddings; the retrieved article does not state a publication date. They are not recommendations for server RAM. If each record stores more than one embedding, calculate each vector field separately and add the results.
Why a vector database needs more than the raw vector bytes
A production index also uses memory or disk for its search index, point identifiers, payloads and payload indexes. How much of that must be resident depends on the database configuration and whether vectors or index structures are kept in RAM, cached, or read from disk. Replicas multiply stored data and can affect the capacity required across the deployment.
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Qdrant’s component-based planning method
Qdrant sizes HNSW memory separately using base × m × 2 × 4 bytes × 1.2; its documented default for m is 16. Its method also calls out a 52-byte ID tracker per point, payloads and payload indexes, replication, and which structures are pinned, cached, or cold. Qdrant suggests about 20% headroom after applicable RAM and disk components are totaled. These are Qdrant-specific planning rules, not universal database constants. See Qdrant’s capacity-planning guide.
Azure AI Search’s algorithm-overhead example
Microsoft’s Azure AI Search guidance estimates size as raw size multiplied by algorithm overhead and deleted-document ratio. Its example starts with 1,000 documents, each with one 1,536-dimensional float vector: 6.144 MB raw. With 10% algorithm overhead and 10% deleted documents, its example rises to 7.434 MB. This is an Azure-specific illustration, not a multiplier to apply to every engine. See Microsoft’s vector-index size guidance.
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How to calculate a practical capacity estimate
- Count vector fields. Use the number of stored records and the dimensions of each vector field. If a record has multiple embeddings, calculate each field independently.
- Choose the stored datatype. Multiply each field’s count and dimensions by its bytes per dimension. Qdrant documents float32 at four bytes, float16 at two, uint8 at one, and Turbo4 at half a byte per dimension. Check that your selected engine and index support the intended datatype.
- Add the index and identifiers. Use the selected engine’s own sizing method for HNSW or other index structures, and account for ID tracking where applicable.
- Account for metadata and filters. Estimate payload size and any payload indexes from the fields actually stored and the filters the application needs. Do not assume all metadata has to be indexed or resident in RAM.
- Model the deployment layout. Include replication and distinguish resident memory from disk-backed or cold data, caches, and other tiers.
- Leave operational capacity and validate. Apply the selected vendor’s capacity guidance, then measure memory use, latency, recall, and throughput with representative data and queries. Raw arithmetic is a starting point, not a workload benchmark.
Ways to reduce resident memory
Use fewer dimensions when the task allows
Raw vector memory scales linearly with dimension count. At float32, a 384-dimensional vector uses one quarter of the vector bytes of a 1,536-dimensional vector. A smaller dimension is useful only if the embedding model and retrieval task still meet quality requirements.
Store vectors in a narrower datatype
Float16 uses half the bytes of float32, while uint8 and Turbo4 use still less according to Qdrant’s datatype documentation. Qdrant reports virtually no impact on vector-search quality for float16 in its documentation, but quality should be validated with the chosen model, data, and implementation. See Qdrant’s optimization documentation.
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Quantize, then measure retrieval quality
Hugging Face’s article reports results for its tested Cohere embed-english-v3.0 setup at 1,024 dimensions across 100 million vectors: float32 used 953.67 GB, int8 used 238.41 GB, and binary used 29.80 GB. Its reported retrieval scores were 55.0, 55.0, and 52.3 respectively. These are results from that particular experiment, not guaranteed memory or quality outcomes for other models and workloads. See Hugging Face’s embedding-quantization article.
Keep full-precision vectors on disk or in a colder tier
Tiered designs can keep a smaller or quantized representation in RAM while retaining original vectors on disk. Qdrant describes keeping original vectors cold while quantized vectors stay in RAM. MongoDB describes keeping quantized vectors in memory and full-precision vectors on disk for rescoring or exact search. The relevant memory savings depend on what remains resident; disk access and the chosen search path also affect latency. See MongoDB’s vector-quantization documentation.
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What to compare before choosing a design
- Dimensions and bytes per dimension for every vector field.
- Full-precision versus quantized storage, including where original vectors live.
- Index type and its engine-specific memory overhead.
- Replication factor and the memory or disk tier used by vectors, indexes, and caches.
- Payload size and which fields need indexes for filtering.
- Measured retrieval quality, latency, recall, and throughput under the intended workload.
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