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Hardware Requirements

Due to the nature of packet analysis and granular drill-down reporting features, Sensei require more horsepower than a standard L3-L4 firewall.

{% hint style="info" %} Sensei requires at least 8 GB of memory. Installer will not continue if you have less than 8 GB of RAM. {% endhint %}

{% hint style="info" %} A roadmap feature - Cloud reporting - will enable you to install Sensei to devices which have limited amount of memory. E.g. you'll be able to install Sensei to a Raspberry Pi. {% endhint %}

CPU & Memory

Because the analytics module relies on Elastic Search to do Big Data processing, amount of the memory available in the system is crucial for the performance of the whole product.

At least dual-core (i5 and equivalent) or preferably quad-core modern CPU (i7 and equivalent) would be advisable.

Recommended minimum hardware requirements for Sensei based on the number of users and the bandwidth:

Number of Users WAN Bandwith Min. Memory Min. CPU
<25 20 Mbps 8 GB Intel Dual-Core i3 2.0 GHz (2 Cores, 4 Threads) or equivalent
25-50 50 Mbps
10 Kpps
8 GB Intel Dual-Core i5 2.0 GHz (2 Cores, 4 Threads) or equivalent

100 Mbps

20 Kpps

16 GB Intel Dual-Core i5 2.2 GHz (2 Cores, 4 Threads) or equivalent

200 Mbps

40 Kpps

16 GB Intel Dual-Core i7 2.0 GHz (2 Cores, 4 Threads) or equivalent

500 Mbps

100 Kpps

32 GB Intel Quad-Core i7 3.40 GHz (4 Cores, 8 Threads) or equivalent
## Disk Space

{% hint style="info" %} Sensei uses Elastic Search Engine as its backend to process the Big Data. Please spare at least 5 MB of disk space per hour per megabit/second throughput. {% endhint %}

If you're running a 100 Mbps link (about 100 users) which is quite active during the daytime and idle rest of the day, you can calculate the space needed as follows:

5 MB x 12 hours x 100 Mbps = 6 GB per day.
6 GB x 7 days a week = 42 GB per week.
42 x 4 weeks a month = 164 GB per month.

As of 0.7.0 (changelog), Sensei retires reports data to open up space for the new coming data. After the configured timespan, existing reports data is automatically purged to save space for fresh data.