Vector Database (HNSW / Float32 / Scalar Quantization) RAM Calculator

Size HNSW vector search index RAM for 768d, 1024d, 1536d, or 3072d embeddings.

Total HNSW Index RAM Required
67.17 GiB
Raw Embedding Vector Payload 57.22 GiB
HNSW Graph Edge Index Overhead 1.19 GiB
Scalar INT8 (1B/dim) RAM Equivalent 17.82 GiB
Inputs
M vectors
Total indexed vectors in millions.
dims
Vector embedding dimension count (e.g., 768, 1024, 1536, 3072).
B/dim
Bytes per dimension component (4=FP32, 2=FP16, 1=SQ8, 0.125=Binary).
M links
HNSW bi-directional graph neighbors per node (M, typically 16 or 32).
Results & Graph
Total HNSW Index RAM Required
67.17 GiB
Raw Embedding Vector Payload
57.22 GiB
HNSW Graph Edge Index Overhead
1.19 GiB
Scalar INT8 (1B/dim) RAM Equivalent
17.82 GiB

Total HNSW Index RAM Required

Graph 1 • Vector Count Millions Sensitivity Recalculates the domain equation across 50% to 150% of the primary input while holding other parameters constant.
0 GiB 54.4 GiB 108.8 GiB 50%: 33.59 GiB 33.6 50% 75%: 50.38 GiB 50.4 75% 100%: 67.17 GiB 67.2 100% 125%: 83.97 GiB 84.0 125% 150%: 100.76 GiB 100.8 150%
Graph 2 • Primary & Supporting Metric Comparison Compares the primary output alongside supporting domain benchmarks.
Total HNSW Index RAM Required 67.17 GiB Raw Embedding Vector Payload 57.22 GiB HNSW Graph Edge Index Overhead 1.19 GiB Scalar INT8 (1B/dim) RAM Equivalent 17.82 GiB
Graph 3 • Exact Output Table & Units Displays each calculated output in its exact domain unit.
Total HNSW Index RAM Required67.17 GiBRaw Embedding Vector Payload57.22 GiBHNSW Graph Edge Index Overhead1.19 GiBScalar INT8 (1B/dim) RAM Equivalent17.82 GiB
Summary: Indexing 10M vectors (1536d, 4B/dim, M=16) requires 67.17 GiB RAM (57.22 GiB raw vectors + 1.19 GiB HNSW graph links).
Summary: Indexing 10M vectors (1536d, 4B/dim, M=16) requires 67.17 GiB RAM (57.22 GiB raw vectors + 1.19 GiB HNSW graph links).
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Formula & Mathematical Methodology: Vector DB HNSW RAM Systems Engineering Formula

RawBytes = Vectors × Dim × BytesPerFloat
HNSWBytes = Vectors × 2 × Mlinks × 4
Total = (Raw + HNSW) × 1.15
RawBytes = Vectors × Dim × BytesPerFloat; HNSW_Bytes = Vectors × 2 × M_links × 4; Total = (Raw + HNSW) × 1.15
  • vectorCountMillions: Vector Count Millions (M vectors)
  • embeddingDimensions: Embedding Dimensions (dims)
  • bytesPerDimension: Bytes Per Dimension (B/dim)
  • hnswMaxLinksM: Hnsw Max Links M (M links)

Rounding: Binary/decimal capacities and engineering metrics retain full floating-point precision and round to 2 decimal places.

Worked Example: Architecture Sizing Example: Vector DB HNSW RAM

Size HNSW vector search index RAM for 768d, 1024d, 1536d, or 3072d embeddings.

  1. Enter the workload parameters, model dimensions, or network link specifications.
  2. Compute the sizing equation: RawBytes = Vectors × Dim × BytesPerFloat; HNSW_Bytes = Vectors × 2 × M_links × 4; Total = (Raw + HNSW) × 1.15.
  3. Inspect the primary capacity metric, headroom buffers, and 3 SVG visualizations.
Result: Calculates Vector DB HNSW RAM via RawBytes = Vectors × Dim × BytesPerFloat; HNSW_Bytes = Vectors × 2 × M_links × 4; Total = (Raw + HNSW) × 1.15.

Frequently Asked Questions

How does the Vector Database (HNSW / Float32 / Scalar Quantization) RAM Calculator compute capacity?

It evaluates RawBytes = Vectors × Dim × BytesPerFloat; HNSW_Bytes = Vectors × 2 × M_links × 4; Total = (Raw + HNSW) × 1.15 directly in your browser and plots three custom SVG diagrams.

Can I compare different cluster or model configurations side by side?

Yes. Use the "Compare (0/3)" button in the results panel to pin up to three configurations.

Does this tool upload any of my architecture parameters?

No. All calculations run 100% client-side in your browser.

Developer Tools · AI & LLM Infrastructure

Vector Database (HNSW / Float32 / Scalar Quantization) RAM Calculator: inputs, method, and how to read the result

Size HNSW vector search index RAM for 768d, 1024d, 1536d, or 3072d embeddings.

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.

  • Vector Count Millions: Total indexed vectors in millions.
  • Embedding Dimensions: Vector embedding dimension count (e.g., 768, 1024, 1536, 3072).
  • Bytes Per Dimension: Bytes per dimension component (4=FP32, 2=FP16, 1=SQ8, 0.125=Binary).
  • Hnsw Max Links M: HNSW bi-directional graph neighbors per node (M, typically 16 or 32).

What the calculation includes

With the default values shown, Total HNSW Index RAM Required is 67.17 GiB. The calculation follows Vector DB HNSW RAM Systems Engineering Formula; the formula and variable definitions above show how the inputs combine.

One-input check: With the other defaults held constant, changing Vector Count Millions from 10 M vectors to 11 M vectors changes Total HNSW Index RAM Required from 67.17 GiB to 73.89 GiB. This is a comparison inside the calculator's stated model, not a prediction.

Where to use caution

  • Uses industry-standard IEEE, IETF RFC, and GPU/cloud hardware sizing conventions.
  • Capacity planning estimate; benchmark under production traffic patterns before finalizing cluster or hardware procurement.

Scope: IEEE / IETF / Cloud Systems Engineering Standard Check the assumptions and limitations against your situation before relying on the estimate.

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