--- title: "Resource Guides | Hardware Sizing | N3uron KB V1.22" slug: "hardware-sizing-guide" description: "This document provides comprehensive guidance on estimating the hardware requirements for running N3uron." updated: 2025-12-22T11:18:24Z published: 2025-12-22T11:18:24Z --- > ## Documentation Index > Fetch the complete documentation index at: https://docs.n3uron.com/llms.txt > Use this file to discover all available pages before exploring further. # Hardware Sizing ## Introduction This document provides comprehensive guidance on estimating the hardware requirements for running **N3uron**. Designed to be lightweight, **N3uron**is capable of managing thousands to tens of thousands of tags on resource-constrained devices like a Raspberry Pi. With more powerful hardware, N3uron scales seamlessly, enabling it to handle hundreds of thousands of tags on a single node, delivering exceptional performance for a wide range of deployment scenarios. To ensure optimal performance and scalability, the following factors should be considered when estimating hardware requirements: - **Number of Tags:**Memory usage increases linearly with the number of tags. - **Event Rate (tag changes per second):**Higher event rates demand more CPU resources. Adjusting scan rates and deadband settings helps optimize performance. - **Modules in Use:** Each module instance creates a new process, impacting systems with limited memory. - **Scripting and Edge Computing:**Complex scripts and long-running calculations can significantly increase CPU and memory usage. - **Data Historization:**Running the Historian database on the same node increases memory usage, requires fast I/O, and reduces event-handling capacity as system resources are shared between N3uron and the database. - **Web Vision Design:**The design of [Web Vision](/v122/docs/web-vision-introduction) screens, including the number of visualized tags, historical data queries, and refresh rates, impacts system performance. > [!NOTE] > Note: > > At the hardware level, high-frequency CPU cores and fast SSD storage ensure the best performance for N3uron. ## Historian Database The N3uron [Historian](/v122/docs/historian-introduction) is a high-performance, space-efficient time-series data historian built on [MongoDB](https://www.mongodb.com/).It is optimized to handle large amounts of data while maintaining minimal storage requirements. For the most common N3uron workloads, the Historian requires**≈7.5 bytes/event***, meaning it can store **~****150,000,000 events using 1 GB of disk space***. **Using Zstd compression, please see*[*Enabling Zstd Compression in MongoDB*](https://docs.n3uron.com/v122/docs/historian-appendix#enabling-zstd-compression-in-mongodb) To estimate the disk space required for your workload, consider the number of events per second (**Event Rate**) to be historized and the duration for which the data will be stored (**Retention Period**). $\text{Storage (GB)} = \frac{Event Rate (events/second) \times Retention Period (seconds)}{150{,}000{,}000}$ Here are some examples: | Event Rate (events/s) | Retention Period (seconds) | Storage (aprox) | | --- | --- | --- | | 500 | 1 month (2,592,000 s) | 8.6 GB | | 2,000 | 6 months (15,552,000 s) | 207 GB | | 1,500 | 3 years (94,608,000 s) | 946 GB | | 20,000 | 5 years (157,680,000 s) | 21 TB | > [!NOTE] > Note: > > Regarding disk type and speed, we strongly recommend using SSD storage for the Historian database to ensure optimal performance and reliability. For workloads with very high event rates, it is crucial to provision fast SSDs with high throughput and IOPS (Input/Output Operations Per Second). ## Edge Deployments > [!NOTE] > Note > > Please make sure you test and validate your solution based on your workload, using the recommended hardware as a reference. ### Small Deployments Recommended hardware for **N3uron**deployments in small plants, handling a low number of signals and events per second, primarily focused on data collection with limited historization, basic computations, and data exchange with other systems. #### 4 CPU cores (ARM), 1GB RAM This is a low-power ARM device, similar to a Raspberry Pi 3/4 or equivalent. **Without a local Historian:** 10,000 tags ~2,000 events per second **With a local Historian:** 5,000 tags ~1,000 events per second #### 4 CPU cores (ARM), 2GB RAM This is a low-power ARM device, similar to a Raspberry Pi 3/4 or equivalent. **Without a local Historian:** 40,000 tags ~2,000 events per second **With a local Historian:** 20,000 tags ~1,000 events per second #### 1 CPU core (4GHz), 1GB RAM **Without a local Historian:** 10,000 tags ~5,000 events per second **With a local Historian:** 5,000 tags ~1,500 events per second #### 1 CPU core (4GHz), 2GB RAM **Without a local Historian:** 40,000 tags ~5,000 events per second **With a local Historian:** 20,000 tags ~1,500 events per second ### Medium Recommended hardware for **N3uron**deployments in medium-sized plants, capable of handling a larger number of signals, performing more complex edge computing, data historization, and providing basic data visualization. #### 4 CPU core (ARM), 8GB RAM This is a more performant ARM device, similar to a Raspberry Pi 5 or equivalent. **Without a local Historian:** 100,000 tags ~6,000 events per second **With a local Historian:** 100,000 tags ~3,000 events per second #### 2 CPU cores (4GHz), 4GB RAM **Without a local Historian:** 70,000 tags ~20,000 events per second **With a local Historian:** 50,000 tags ~7,000 events per second #### 2 CPU cores (4GHz), 8GB RAM **Without a local Historian:** 150,000 tags ~20,000 events per second **With a local Historian:** 150,000 tags ~10,000 events per second ### Large Recommended hardware for **N3uron**deployments in large plants, designed to manage the most demanding workloads, including a high volume of signals and events per second, robust data historization, and advanced visualization capabilities. #### 4 CPU cores (4GHz), 8GB RAM **Without a local Historian:** 200,000 tags ~50,000 events per second **With a local Historian:** 200,000 tags ~20,000 events per second #### 4 CPU cores (4GHz), 16GB RAM **Without a local Historian:** 500,000 tags ~60,000 events per second **With a local Historian:** 500,000 tags ~30,000 events per second #### 8 CPU cores (4GHz), 16GB RAM **Without a local Historian:** 500,000 tags ~100,000 events per second **With a local Historian:** 500,000 tags ~50,000 events per second ## Cloud Deployments > [!NOTE] > Note > > Please make sure you test and validate your solution based on your workload, using the recommended hardware as a reference. The recommended architecture for deploying **N3uron**on a central server, whether in the cloud or on-premise, is to use at least two machines: one dedicated to running **N3uron**and another exclusively for the **Historian database**. The examples provided below use **Amazon Web Services (AWS)**instances as a reference for the **N3uron node**, but equivalent configurations can be selected on other cloud platforms. ### Small #### 2 vCPU, 8GB RAM This corresponds to the AWS **m7g.large**or equivalent instance. 200,000 tags ~30,000 events per second ### Medium #### 4 vCPU, 16GB RAM This corresponds to the AWS **m7g.xlarge**or equivalent instance. 500,000 tags ~50,000 events per second ### Large #### 8 vCPU, 32GB RAM This corresponds to the AWS **m7g.2xlarge**or equivalent instance. 1,000,000 tags ~100,000 events per second