Vector Database & HNSW Index RAM Calculator (Embeddings, Dimensions & Quantization)

Size RAM (GB) for vector embeddings and HNSW graph links across FP32, FP16, and INT8 quantization.

Total In-Memory HNSW Index RAM
73.6 GB RAM
Raw Embedding Vector Payload 61.44 GB
HNSW Graph Adjacency Links (M=16) 2.56 GB
RAM If Scalar Quantized (SQ8 = 1 B/dim) 20.608 GB
Inputs
⚡ Scenarios:
M vectors
Total number of indexed embedding vectors in Millions.
dims
Vector dimension length (e.g., 384, 768, 1024, 1536 OpenAI small, 3072 large).
bytes/dim
Bytes per dimension element (4 = FP32, 2 = FP16, 1 = Scalar INT8 Quantization).
M links
HNSW parameter M (bidirectional graph edges per node; typically 16–32).
Results & Graph
Total In-Memory HNSW Index RAM
73.6 GB RAM
Raw Embedding Vector Payload
61.44 GB
HNSW Graph Adjacency Links (M=16)
2.56 GB
RAM If Scalar Quantized (SQ8 = 1 B/dim)
20.608 GB

Total In-Memory HNSW Index RAM

Graph 1 • Vector DB & HNSW RAM Sizing • Domain Architecture & Live Telemetry Maps Total Vectors Millions, Embedding Dimensions, Bytes Per Dimension Element through Vector DB & HNSW RAM Sizing Formula.
Total In-Memory HNSW Index RAM: 73.6 GB RAM Vector DB & HNSW RAM Sizing • Invariants & Metrics Total In-Memory HNSW Index R 73.6 GB RAM Raw Embedding Vector Payload 61.44 GB HNSW Graph Adjacency Links ( 2.56 GB RAM If Scalar Quantized (SQ8 20.608 GB
Graph 2 • Total In-Memory HNSW Index RAM Across Total Vectors Millions Sweep (40% → 160%) Evaluates RawVectorBytes = NumVectors × Dimensions × BytesPerFloat; HNSW_GraphBy across 7 operating points (current input highlighted).
0 GB RAM 64.77 GB RAM 129.5 GB RAM 29.44 4 44.16 6 58.88 8 73.60 10 88.32 12 103.0 14 117.8 16
Graph 3 • Joint Sensitivity: Total Vectors Millions × Embedding Dimensions Compares Total In-Memory HNSW Index RAM when Embedding Dimensions is shifted +25% (solid) vs. −25% (dashed).
0 GB RAM 65.25 GB RAM 130.5 GB RAM --- Embedding Dimensions @ 75% 63.88 -30% 77.57 -15% 91.26 +0% 105.0 +15% 118.6 +30%
Summary: Indexing 10M vectors of 1536 dimensions (4 B/dim, M=16) requires 61.44 GB raw vectors + 2.56 GB HNSW graph = 73.6 GB RAM (20.61 GB with INT8 scalar quantization).
Summary: Indexing 10M vectors of 1536 dimensions (4 B/dim, M=16) requires 61.44 GB raw vectors + 2.56 GB HNSW graph = 73.6 GB RAM (20.61 GB with INT8 scalar quantization).
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Formula & Mathematical Methodology: Vector DB & HNSW RAM Sizing Formula

RawVectorBytes = NumVectors × Dimensions × BytesPerFloat
HNSWGraphBytes = NumVectors × MLinks × 2 × 8
TotalRAMGB = (Raw + HNSW) × 1.15 / 10⁹
RawVectorBytes = NumVectors × Dimensions × BytesPerFloat; HNSW_GraphBytes = NumVectors × M_Links × 2 × 8; TotalRAM_GB = (Raw + HNSW) × 1.15 / 10⁹
  • totalVectorsMillions: Total number of indexed embedding vectors in Millions.
  • embeddingDimensions: Vector dimension length (e.g., 384, 768, 1024, 1536 OpenAI small, 3072 large).
  • bytesPerDimensionElement: Bytes per dimension element (4 = FP32, 2 = FP16, 1 = Scalar INT8 Quantization).
  • hnswBidirectionalLinksM: HNSW parameter M (bidirectional graph edges per node; typically 16–32).

Rounding: Internal floating-point calculations run in 64-bit IEEE-754 double precision; display values round to 2–4 decimal places.

Worked Example: Vector DB & HNSW RAM Sizing Worked Example

Size RAM (GB) for vector embeddings and HNSW graph links across FP32, FP16, and INT8 quantization.

  1. Enter the input measurement, system configuration, or astronomical parameter.
  2. Apply the standard conversion or analytical equation: RawVectorBytes = NumVectors × Dimensions × BytesPerFloat; HNSW_GraphBytes = NumVectors × M_Links × 2 × 8; TotalRAM_GB = (Raw + HNSW) × 1.15 / 10⁹.
  3. Inspect the primary metric, secondary equivalents, and 3 real-time SVG graphs.
Result: Computes Vector DB & HNSW RAM Sizing via RawVectorBytes = NumVectors × Dimensions × BytesPerFloat; HNSW_GraphBytes = NumVectors × M_Links × 2 × 8; TotalRAM_GB = (Raw + HNSW) × 1.15 / 10⁹.

Frequently Asked Questions

How does the Vector Database & HNSW Index RAM Calculator (Embeddings, Dimensions & Quantization) perform the calculation?

It evaluates RawVectorBytes = NumVectors × Dimensions × BytesPerFloat; HNSW_GraphBytes = NumVectors × M_Links × 2 × 8; TotalRAM_GB = (Raw + HNSW) × 1.15 / 10⁹ directly in your browser and renders three custom SVG charts in real time.

Can I solve backwards for an input parameter using Goal Seek?

Yes. Click "🎯 Goal Seek" in the results toolbar to solve backwards for any numeric input field.

Does this tool work offline?

Yes. All calculations run 100% locally in your browser with full offline PWA support.

Developer Tools · AI, Cloud & Distributed Systems

Vector Database & HNSW Index RAM Calculator (Embeddings, Dimensions & Quantization): inputs, method, and how to read the result

Size RAM (GB) for vector embeddings and HNSW graph links across FP32, FP16, and INT8 quantization.

Choose inputs for the case you are checking

The defaults are illustrative. Replace them with values for your case and keep the units shown beside each field.

  • Total Vectors Millions: Total number of indexed embedding vectors in Millions.
  • Embedding Dimensions: Vector dimension length (e.g., 384, 768, 1024, 1536 OpenAI small, 3072 large).
  • Bytes Per Dimension Element: Bytes per dimension element (4 = FP32, 2 = FP16, 1 = Scalar INT8 Quantization).
  • Hnsw Bidirectional Links M: HNSW parameter M (bidirectional graph edges per node; typically 16–32).

What the calculation includes

With the default values shown, Total In-Memory HNSW Index RAM is 73.6 GB RAM. The calculation follows Vector DB & HNSW RAM Sizing Formula; the formula and variable definitions above show how the inputs combine.

One-input check: With the other defaults held constant, changing Total Vectors Millions from 10 M vectors to 11 M vectors changes Total In-Memory HNSW Index RAM from 73.6 GB RAM to 80.96 GB RAM. This is a comparison inside the calculator's stated model, not a prediction.

Where to use caution

  • Uses official NIST Special Publication 811, IEEE/RFC systems engineering, APA psychometric, and USNO astronomical formulas.
  • Empirical metallurgy and calendar conversions represent standard reference-condition approximations.

Scope: Global NIST, IEEE, APA & Astronomical Standard Check the assumptions and limitations against your situation before relying on the estimate.

More in Developer Tools: Unit Conversion Calculator, Scientific Calculator, Date Difference & Business Days Calculator.