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RESEARCH 》 Power consumption of my Home Lab devices for research

AMD RYZEN 3 1200 - FreeNAS Storage array build
  • CPU: AMD Ryzen 3 1200 (4 cores/4 threads)
  • RAM: Corsair Vengeance 8GB DDR4 LPX 2400MHz C16 Kit
  • Motherboard: Gigabyte GA-A320M-HD2 AM44
  • Graphics/Display: Asus Geforce 210GT 1GB DDR3
  • PSU: Circle CPH698V12-400
  • Storage: WDC WD10JPVX-75JC3T0 - WD 1TB HDD
System BIOS53 watts
Idle System (Linux Ubuntu OS)52 watts
Casual browsing53 watts
Youtube video playback60 watts
Kernel compilation with 4-threads "make -j4" (99% load)74 watts
Kernel compilation with 3-threads "make -j3" (80% load)71 watts

My Intel Core i7-5820K - Desktop build
  • CPU: Intel Core i7-5820K (6 cores/12 threads)
  • RAM: Corsair PC2800 DDR4 14GB Kit
  • Motherboard: Gigabyte X99-UD4
  • Graphics/Display: Asus Geforce 210GT 1GB DDR3
  • PSU: Corsair VS450
  • CPU Liquid Cooling system: Cooler Master Nepton 240m
  • Storage: Transcend TS128GSSD370 128GB SSD
Idle System (Linux Ubuntu OS)70 watts
System BIOS90 watts
Linux kernel compilation (80%) load150 watts

My Intel Celeron CPU 1037U Mini PC WAN Optimization Device
  • CPU: Intel Celeron CPU 1037U
  • RAM: DDR3 PC3L 4GB
  • Storage: Transcend TS128GSSD370 128GB SSD
Idle System (Linux Ubuntu OS)18-20 watts
System BIOS16.5 watts
Linux kernel compilation (95%) load21-24 watts

My HP Envy 15-J111TX Laptop
  • CPU: Intel Corei7-4700MQ
  • RAM: DDR3 PC3L 12GB
  • Storage: WD Blue 250GB Scorpio HDD
Idle System (Linux Ubuntu OS) charging44 watts
Idle System (Linux Ubuntu OS) charged15 watts
Poweroff charging28 watts
Poweroff charged0.1 watts
Poweron charged suspend0.75 watts
Linux kernel compilation (95%) load charging90 watts
Linux kernel compilation (95%) load charged69 watts

My Dell 15R 5537 Laptop
  • CPU: Intel Corei7-4500U
  • RAM: DDR3 PC3L 8GB
  • Storage: Seagate 320GB Momentus HDD
Idle System (Linux Ubuntu OS) charging42 watts
Idle System (Linux Ubuntu OS) charged10 watts
Poweroff charging29 watts
Poweroff charged0.1 watts
Poweron charged suspend0.70 watts
Linux kernel compilation (95%) load charging60 watts
Linux kernel compilation (95%) load charged30 watts

My Acer Aspire 4810T Laptop
  • CPU: Intel Core Solo SU3500 1.4 GHz
  • RAM: DDR3 PC3 4GB
  • Storage: WD Blue 250GB Scorpio HDD
  * No Battery, so no charging.
Idle System (Linux Manjaro OS)16.23 watts
System BIOS24.30 watts
Casual Browsing22.27 watts
Youtube Playback22.45 watts

Raspberry Pi2 Device
  • Powered via 2Amp USB power-supply
  • Raspbian OS
  • USB mouse and USB keyboard connected
Casual browsing2.6 - 3 watts
Youtube video playback (25% load)3 - 3.5 watts
Kernel compilation with 4-threads "make -j4" (99% load)3.9 - 4 watts
Kernel compilation with 3-threads "make -j3"3.67 - 3.75 watts
idle device with no keyboard and no mouse2.08 - 2.1 watts

NETGEAR RN104 ReadyNAS
  • 2x 2.5'' Laptop HDD drives
  • 2x 3.5'' Desktop HDD drives
  • Single x-RAID volume with 4 HDD drives
Device off but plugged-in0.58 watts
Idle device after booting28 watts
File copy (write operation)28.7 watts
RAID Volume scrub operation29.5 watts

APC BX600C-IN UPS - APC Back-UPS 600(UPS not powered-on but connected to live power socket)
Standby Charging13.5 watts
Standby not-Charging7.8 watts

APC BX600CI-IN UPS - APC Back-UPS 600(UPS not powered-on but connected to live power socket)
Standby Charging9.5 watts
Standby not-Charging10-0.9 watts

BenQ LED Monitor 24'' GW2470HM
off plugged-in0.00 watts
Dim11.7 watts

LG LCD TV Monitor 23'' M237WA-PT
off plugged-in0.8 watts
Dim33 watts
Bright45 watts

Samsung LCD Monitor 22'' 2243NWX
off plugged-in0.7 watts
Dim20 watts
Bright33.5 watts

Power consumption of my Home Lab devices for research

Here is my power-consumption measurements of various devices deployed within my home lab. I measured via my kill-a-watt sort of power-meter which is fairly reliable and accurate. I checked its accuracy with various standard load such as Philips LED laps and other constant power-consuming devices to make sure that the power-meter is precise.

So far I maintained this data in my personal Google drive spreadsheet documents. But now I thought perhaps its good to share these numbers so that it is useful for various users to access their equipment such as:

  • decide UPS and battery backup ratings
  • off-grid solar power installations
  • choose new upgraded hardware which consumes less power and deliver better performance such as SSD over traditional HDD, new CPU, new Monitor, new laptop, servers, desktops and so on. And discard obsolete old hardware.
  • choosing the right PSU (power supply unit) for your desktop PC build

Before posting this article I shot a VLOG regarding the same and posted in my Youtube channel The Linux Channel. You can kindly watch the same:

Explore my lab's historical month wise power-usage trends: I started logging my entire lab monthly power-consumption readings. You can read the article HERE.

Off-Grid Solar Power System for Raspberry Pi: When you choose to use your Raspberry Pi device as your IoT based remote weather station or if you are building Linux kernel (like kernel compilation) within the same, you need a good uninterrupted power source (UPS). But if you are using it on site or in some research camping location you can choose to power your Raspberry Pi device with your custom off-grid solar power source. Kindly read my complete article about the same HERE.
Off-Grid Solar Power System for Raspberry Pi



Suggested Topics:


Generic Home Lab Research

💎 TOFFEE-MOCHA new bootable ISO: Download
💎 TOFFEE Data-Center Big picture and Overview: Download PDF


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TOFFEE-Butterscotch Documentation :: TOFFEE-Butterscotch-1.0.11-rpi2-23-nov-2016 ↗
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It is quite interesting that there are few web hosting firms are offering direct CDN based hosting services. Since it is a direct CDN based hosting, it is cheap, extremely easy or transparent CDN service. It is transparent, since each time you publish your content in the hosting web-server (origin server), it is immediately is in sync automatically in the user-serving CDN caching machines. Since the hosting vendor and the CDN vendor are all the same, it is also easy to use their services. There is no incompatibility issues, interoperability issues, and better integrated analytics, are all the benefits of CDN Hosting services.

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In case if you need to deploy TOFFEE along with your existing VPN devices you can deploy the same as shown below. This will allow your VPN devices to encrypt your TOFFEE WAN Optimized network data. NOTE: Make sure about the VPN deployment topology done in the right order. Else TOFFEE (LAN side) may get VPN encrypted packets which may not be possible (and or difficult) to further optimize. Hence always make sure to deploy them in a topology suggested below so that TOFFEE devices are out of VPN tunnel.

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TOFFEE-Butterscotch Bandwidth saver software development - Update: 17-Nov-2016 ↗
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TOFFEE Documentation :: TOFFEE-1.1.24-3-rpi2 ↗
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Here is my VLOG Youtube video of the same which includes details about version release notes, future road-map and so on. The TOFFEE release is highly optimized and customized for hardware platforms such as x86-64 based Intel NUC and other Intel mobile computing platforms such as laptops and so on. This version (or release) is not suited and so not recommended to be used for high-end desktop and server hardware platform.

在YouTube上观看 - [1888//1] Deep Space Communication - Episode1 - Introduction ↗


TOFFEE (and TOFFEE-DataCenter) optimized Wireless Mesh-Networks - B.A.T.M.A.N [open-mesh.org (Open Mesh)] ↗
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TOFFEE-DataCenter WAN Optimization - Google Hangouts Demo and VOIP Optimization ↗
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TOFFEE-Butterscotch Documentation :: TOFFEE-Butterscotch-1.0.11-rpi2-23-nov-2016 ↗
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TOFFEE-DataCenter screenshots on a Dual CPU - Intel(R) Xeon(R) CPU E5645 @ 2.40GHz - Dell Server ↗
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TOFFEE-Butterscotch Documentation :: TOFFEE-Butterscotch-1.0.11-rpi2-23-nov-2016 ↗
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TOFFEE-DataCenter :: Optimized ISP backbone networks for countries with slowest Internet Speed ↗
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Research :: Optimization of network data (WAN Optimization) at various levels:
Network File level network data WAN Optimization


Learn Linux Systems Software and Kernel Programming:
Linux, Kernel, Networking and Systems-Software online classes [CDN]


Hardware Compression and Decompression Accelerator Cards:
TOFFEE Architecture with Compression and Decompression Accelerator Card


TOFFEE-DataCenter on a Dell Server - Intel Xeon E5645 CPU:
TOFFEE-DataCenter screenshots on a Dual CPU - Intel(R) Xeon(R) CPU E5645 @ 2.40GHz - Dell Server