Vector Database Storage & RAM Estimator
Calculate RAM and NVMe SSD capacity required for HNSW, DiskANN, Product Quantization (PQ), and pgvector similarity search.
⚙️ Dataset & Index Settings
Vector Count
1,000,000
Embedding Dimensions
Index Algorithm
Metadata Payload Size / Vector
500 Bytes
📊 Capacity & Memory Output
Raw Vector Coordinates
Uncompressed FP32 embedding bytes
5.72 GB
Graph Index Overhead
HNSW M=32 edge list memory
1.20 GB
Metadata Storage
JSON document attributes
0.47 GB
Total Required Server RAM
7.39 GB
💡 Single Database Node: 16 GB RAM Server
Frequently Asked Questions (FAQ)
Q1: Why does HNSW require so much RAM?
HNSW maintains an in-memory multi-layer graph skip-list. For each vector, HNSW stores up to $M$ bidirectional graph edges ($M \in [16, 64]$) in RAM alongside raw FP32 coordinates, requiring $\sim 1.25 \times$ raw vector memory.
Q2: How does DiskANN achieve billion-scale vector search on SSDs?
DiskANN stores compressed Product Quantization (PQ) vectors in RAM while maintaining the uncompressed graph structure on NVMe SSD drives. Using asynchronous `io_uring` direct I/O, DiskANN executes vector queries in sub-10 milliseconds without consuming terabytes of expensive RAM.
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