AVAILABLE FOR WEB, FLUTTER & QA PROJECTS
How to use this tool
1. Specify Number of Vectors
Enter total vector count in your index (e.g. 100k, 1M, 10M embeddings).
2. Choose Vector Dimensions
Select standard model dimensions: 384 (MiniLM), 768 (BERT/bge), 1536 (OpenAI text-embedding-3-small), or 3072 (text-embedding-3-large).
3. Choose Quantization Precision
Select Float32 (4 bytes), Float16 (2 bytes), or Int8 quantized (1 byte per dimension).
4. Review Memory & HNSW Index RAM
Analyze raw vector storage, HNSW index graph overhead, and minimum RAM required to keep the index in memory.
Formula or logic used
Vector Memory & HNSW Index Mathematics
Vector memory consumption equals dimension count multiplied by byte precision per dimension, plus graph index overhead for approximate nearest neighbor (ANN) search.
Bytes per Vector: $B_{\text{vec}} = D \times B_{\text{precision}}$ (Float32 = 4B, Float16 = 2B, Int8 = 1B)Raw Vector Storage (MB): $\text{Storage} = \frac{N \times B_{\text{vec}}}{1024^2}$HNSW Index Graph Overhead: $\approx 1.5\times \text{ to } 2.0\times$ raw vector memory for graph edgesTotal In-Memory RAM Needed = $\text{Raw Vectors} + \text{HNSW Graph} + 20\% \text{ OS buffer}$
Examples
Example 1: 1,000,000 Vectors with OpenAI text-embedding-3-small (1536-d)
Fits comfortably on a standard 16GB or 32GB RAM cloud instance or managed Pinecone standard pod.
Example 2: 10,000,000 Vectors with Int8 Scalar Quantization (768-d)
Quantization reduces memory footprint by 75% with less than 1% degradation in retrieval accuracy.
Common use cases
Vector DB RAM Allocation
Size server RAM to ensure vector indexes remain memory-resident for sub-50ms query latency.
Scalar & Product Quantization Planning
Evaluate whether quantizing from float32 to int8 saves enough cloud infrastructure cash.
Model Dimension Comparison
Compare the 2x memory increase of moving from 1536-d to 3072-d embeddings against accuracy gains.
Pinecone / Qdrant / Milvus Cluster Sizing
Plan cluster node counts and replica memory budgets for enterprise scale.
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Frequently asked questions
Why does HNSW index take up so much memory?
Hierarchical Navigable Small World (HNSW) creates multi-layer geometric graphs connecting neighboring vectors. Storing bidirectional graph links (typically 16 to 64 edges per node) requires significant auxiliary RAM.
What is the difference between 1536 and 3072 dimensions?
3072 dimensions provide higher expressiveness and nuanced domain representation, but require twice the memory, twice the disk storage, and increase vector dot-product latency.
Does int8 quantization hurt retrieval accuracy?
In practice, scalar quantization (SQ) to int8 typically retains 98.5% to 99.5% of full precision recall while reducing RAM requirements by 75%.
Can vector search run from SSD disk instead of RAM?
Yes, systems like DiskANN and Qdrant support on-disk vector storage with in-memory caching. However, pure SSD lookups are 5x to 10x slower than RAM-resident indexes.
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