---
categories:
- docs
- develop
- stack
- rs
- rc
- oss
- kubernetes
- clients
description: Learn how to optimize memory consumption in Redis vector sets
linkTitle: Memory optimization
title: Memory optimization
weight: 25
---

## Overview

Redis vector sets are efficient, but vector similarity indexing and graph traversal require memory tradeoffs. This guide helps you manage memory use through quantization, graph tuning, and attribute choices.

## Quantization modes

Vector sets support three quantization levels:

| Mode       | Memory usage  | Recall | Notes                           |
|------------|---------------|--------|---------------------------------|
| `Q8`       | 4x smaller    | High   | Default, fast and accurate      |
| `BIN`      | 32x smaller   | Lower  | Fastest, best for coarse search |
| `NOQUANT`  | Full size     | Highest| Best precision, slowest         |

Use `Q8` unless your use case demands either ultra-precision (use `NOQUANT`) or ultra-efficiency (use `BIN`).

## Graph structure memory

HNSW graphs store multiple connections per node. Each node:

- Has an average of `M * 2 + M * 0.33` pointers (default M = 16).
- Stores pointers using 8 bytes each.
- Allocates ~1.33 layers per node.

> A single node with M = 64 may consume ~1 KB in links alone.

To reduce memory:

- Lower `M` to shrink per-node connections.
- Avoid unnecessarily large values for `M` unless recall needs to be improved.

## Attribute and label size

Each node stores:

- A string label (element name)
- Optional JSON attribute string

Tips:

- Use short, fixed-length strings for labels.
- Keep attribute JSON minimal and flat. For example, use `{"year":2020}` instead of nested data.

## Vector dimension

High-dimensional vectors increase storage:

- 300 components at `FP32` = 1200 bytes/vector
- 300 components at `Q8` = 300 bytes/vector

You can reduce this using the `REDUCE` option during [`VADD`]({{< relref "/commands/vadd" >}}), which applies [random projection](https://en.wikipedia.org/wiki/Random_projection):

{{< clients-example set="vecset_tutorial" step="add_reduce" description="Dimension reduction: Use the REDUCE option with VADD to apply random projection and reduce vector dimensions when you need to optimize memory usage while maintaining search quality" difficulty="advanced" >}}
>VADD setNotReduced VALUES 300 ... element
(integer) 1
> VDIM setNotReduced
(integer) 300

>VADD setReduced REDUCE 100 VALUES 300 ... element
(integer) 1
> VDIM setReduced
(integer) 100
{{< /clients-example >}}

This projects a 300-dimensional vector into 100 dimensions, reducing size and improving speed at the cost of some recall.

## Summary

| Strategy            | Effect                                   |
|---------------------|------------------------------------------|
| Use `Q8`            | Best tradeoff for most use cases         |
| Use `BIN`           | Minimal memory, fastest search           |
| Lower `M`           | Shrinks HNSW link graph size             |
| Reduce dimensions   | Cuts memory per vector                   |
| Minimize JSON       | Smaller attributes, less memory per node |

## See also

- [Performance]({{< relref "/develop/data-types/vector-sets/performance" >}})
- [Scalability]({{< relref "/develop/data-types/vector-sets/scalability" >}})