Vector Storage Calculator (RAM / disk for embeddings)
Estimate how much memory or disk your embeddings need — enter vector count, dimensions, and precision (float32/float16/int8/binary) to see raw and indexed size. 100% local.
Last updated:
CommentsEnter your vector count, dimensions, and numeric precision, and see how much RAM or disk the embeddings need — both raw and with a typical index overhead. Quantization options show the savings. Runs locally.
Bytes / vector
6.00 KB
Raw vectors
5.72 GB
With HNSW index (~1.5×)
8.58 GB
Raw size is count × dimensions × bytes-per-dim. The index estimate applies a typical ~1.5× HNSW overhead; IVF and flat indexes differ. Quantization (int8 / binary) trades recall for a big memory saving. All math runs in your browser.
What is Vector Storage Calculator?
Before standing up a vector database you need to know whether a million 1536-dimension vectors fit in memory or need disk — and whether quantization is worth it. Raw storage is simply count × dimensions × bytes-per-dimension: float32 is 4 bytes per dimension, float16 is 2, int8 is 1, and binary quantization is a single bit. On top of that, ANN indexes like HNSW add roughly 1.5× overhead for their graph links. This calculator turns those numbers into concrete GB figures so you can size instances, estimate cost, and decide whether int8 or binary quantization is needed.
How to estimate vector storage
- 1Enter the number of vectors and their dimensions (presets provided).
- 2Pick a precision — float32, float16, int8, or binary.
- 3Read the raw size and the estimate with HNSW index overhead.
Key Concepts
- Quantization
- Storing vectors at lower precision (int8/binary) to cut memory, trading some recall.
- HNSW
- A popular ANN index whose graph links add storage on top of the raw vectors.
Frequently Asked Questions
How is raw vector size calculated?
count × dimensions × bytes-per-dimension. float32 is 4 bytes/dim, float16 is 2, int8 is 1, and binary quantization is 1 bit (0.125 bytes) per dimension.
Why is the indexed size larger than raw?
Approximate-nearest-neighbor indexes like HNSW store graph links alongside the vectors, typically adding ~1.5× overhead. IVF and flat indexes differ; treat the estimate as a planning figure.
Does quantization hurt quality?
int8 and binary quantization drastically cut memory but trade some recall. Many vector databases support it with a re-ranking step to recover most of the accuracy.
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