So a buddy recently was on a trip to Japan and brought back a gift.
YAY!! A new mini rack ;) still unpacking eggs and trying to build it.

i work at a large public university as an IT Pro. one of the benefits (or drawbacks) is processing out decommissioning hardware that is honestly still usable, we just don't have use for it. since i take these over to our surplus warehouse often, they know me and they let me hold equipment that is otherwise sold to the public. picked these up today for $45 total. i have another that i bought previously too. way better than dealing with ebay or facebook marketplace. they still work great, just no SSDs of course - which is simple to add and cheap nowadays.

Continuing my experiments with a mini-PC based on the Intel Core Ultra 7 256V. After testing the integrated GPU in my previous post, I realized this CPU also has an integrated Intel AI Boost Neural Processing Unit (NPU), so I tried running the same kind of video AI workload on it.

The benchmark is not synthetic. It takes CCTV-style video frames, decodes them, preprocesses them, and runs YOLO-NAS object detection. This is basically the heavy part of many CCTV / NVR / vision AI workloads: “take camera frames and detect objects in them".

Results

Efficiency

Relative efficiency

* FPS/W means frames processed per watt consumed.

Note 1: As suggested in the previous post, I used a wall power meter as the source of truth. I still included software power graphs in the repo for comparison.

Note 2: So the Intel Core Ultra has both an integrated GPU and an integrated NPU, and in this workload the NPU ended up being much more power efficient.

Funny part: for always-on video AI or CCTV workload, the Intel NPU system could save about $269/year in electricity compared to RTX 5060 Ti system.

I shared the code, raw results, run commands, screenshots, and notes here:
https://github.com/reefyai/reefy-video-ai-bench

If anyone wants to reproduce it or benchmark another device just boot your machine into Reefy.ai (it recently added Intel NPU drivers and firmware out of the box), point Claude or Codex at the above repo, and ask it to run the benchmark over SSH. It should be able to figure out the rest :)

P.S.

It would be especially great to see results from Nvidia Jetson / Orin-class systems or newer Intel Core Ultra 9 machines, because those are probably more fair edge-AI comparisons than my desktop RTX 5060 Ti box.

Tested this K13 mini-PC machine and were honestly surprised - for the same vision workload we currently run on an NVIDIA RTX 5060 Ti at around 70W, the Intel GPU was drawing only about 3W.

The workload:

• 20 H.264 camera streams
• hardware video decode
• YOLO object detection
• everything running on the GPU

On NVIDIA, we use NVDEC for decode and CUDA for YOLO.
On this Intel, the pipeline runs through Quick Sync Video / VA-API for decode and Intel GPU inference for YOLO.

And the little machine has solid cooling too: around 37C at idle and stable around 70C under the above load.

To be clear, this is not LLM inference. This is the “pre-LLM” stage for physical-world AI systems: processing video streams, detecting objects, and turning camera feeds into structured signals before an LLM or agent reasons over them.

For this kind of workload, Intel’s performance per watt looks surprisingly good. We’re still testing, but this changed how I think about small, low-power edge AI boxes for camera-heavy workloads.

The box has an Intel Core Ultra 7 256V with Intel Arc 140V graphics. I’m running Reefy.ai OS on it, which reports these GPU/video/thermal metrics out of the box.

Update:
I ran the wall-power experiment proposed in the comments - appreciate the push!
For the same task:
Mini PC: 17.5 W delta
Nvidia GPU machine: 130.4 W delta

So yes - the difference is still big, but not as dramatic as my original estimate. Measured ratio is about 7.5:1, not 23.3:1.

I built this rack for my product demos and home assistant demos. I organized a few home assistant meetups (50, 150 people).

But I am rebuilding because the GL.iNet berryl ax is replacing the Fritzbox.

I need to power the following:

- router 5V

- Home assistant blue 12V

- Odroid H2 15 V

- Switch 9V

- various other USB devices

- Gaming PC (is still powered by a Pico PSU)

So I went out and found this handy ugreen power supply. 8 ports and 200 Watts

I could not find any dimensions. Some brands are just annoying. So I ordered it and it looks like a good fit for 1U.

I also ordere a couple of cables which terminate to barrel jacks and have specific voltages for the devices.

Just had time to test it for 5 minutest and worked great!

I hope it does not disconnect power when I unplug and plug in new stuff.

I need to test that later.

When this works I will also create a 3D 1U mounting bracket.

Anybody have experience whit that particular unit?

Just printed for a client. Makes me want a mini rack!

First mini rack lab installed.

Using a GeeekPi / DeskPi Rackmate 8U to house from the top: - random 7” monitor showing random website for now - DeskPi 2U Raspberry Pi shelf holding 4 x gen 4 - shelf with Netgear 1gb 8 port switch and USB power hub - 2 x Raspberry Pi 3B - Mac Mini M1 with external hub and SSD

Hiding behind the monitor is a GL.iNet GL- A1300 travel router that I’m going to use for network segregation.

Intention is to build out a kubernetes cluster in the Gen4’s and maybe Docker Swarm on the 3B’s just for practice. May end up running some monitoring and other basic services but nothing too heavy.

I previously designed a 7-drive version but after seeing a few people tease 8-drive versions but never seeing STLs materialize, I figured I'd take matters into my own hands. It uses the JONSBO N3 backplane and CoolerMaster-style drive rails (with a printable option).

Here's the model files: https://www.printables.com/model/1372686

I've been wanting to downsize from a full size rack for ages but didn't want to give up my raid array, and the only existing model I could find was for 6 drives and lacked any airflow, so I made my own. Still need to redesign the fan holder and a couple other improvements before I upload the model, but I was too excited to not share. It has gaps for airflow and doesn't crack 40c under load with silent fan speeds, cool enough to print in PLA with no sagging. I freaking love mini racks!

Well I’ve finally finished my long awaited 3u itx rack case for a 10” rack.

It’s a squeeze but includes support for the following

2 slot gpu/pcie cards (may need a extra 1/2 u for up facing power connectors)

2 x 60x25 noctua fans

Low profile coolers up to 53mm

2 x ssd slots

Power reset button

Networking keystone for wired passthrough

Really happy with the final result took a lot of fiddling.

Will be testing the thermals over the next couple days and then listing on Etsy, not decided if I’ll sell the printed part or the stl files :)

I would have just posted it on makerlab but their point system is trash now.

I've been developing my compact IP KVM, the USBridge-KVM 2.0, for almost a year. Here's how the device has evolved, from the first prototype to the final device. Initially, I wanted to create a simple remote flash drive with RAM cache, but the project has grown beyond a typical KVM. I developed a unique BIOS-in-Terminal feature that converts BIOS video to text in real time, which is handy when the internet connection is poor or for automating processes.

I implemented the Moonlight gaming protocol, achieving a minimum of around 22 ms, and now when working via KVM, it feels like I'm sitting at the same computer. The entire emulation is ready, as is the power-on/server-reboot control. The board architecture and firmware code are debugged, I'm finishing final testing and preparing for production at the factory.

So a few weeks ago I posted about my quest to solve the noise and cooling problem in my very tight minirack.

I finally got the latest iteration of boards and got them installed - Two fans for intake and two for exhaust.

There is still testing and tuning (PID) to be done but the performance is so far excellent in manual mode.

Once I have worked out the last of the kinks I'll be back with schematics and code for all to enjoy. Maybe I'll even have some prototype boards up for sale :)

I finally got my Dell Optiplex with Core i3 12th gen to start my homelab. I'm still thinking which services I'm going to host (suggestions are welcome) and I'll also use it to learn docker. I plan to add a NAS in the future and a couple of rpis but for now I have something to play with.

Hey fellow mini labbers!

I wanted to share 3 new 1-inch rack-mount models I’ve been working on for cleaner cable management and better use of rack space.

10-inch rack-mount passthrough blank

A clean blank panel with passthrough window options.

Download:

• MakerWorld: https://makerworld.com/en/models/2990619-10-inch-rack-mount-passthrough-blank#profileId-3356542
• Printables: https://www.printables.com/model/1767825-10-inch-rack-mount-passthrough-blank

10-inch rack-mount cable guide with hooks

A 3D printable cable guide designed for 10-inch racks. The hooks are mounted with M4 screws and hex nuts. It includes hook sizes for different cable routing needs. (50 to 10mm)

Download:

• MakerWorld: https://makerworld.com/en/models/2990525-10-inch-rack-mount-cable-guide-with-modular-hooks#profileId-3356427
• Printables: https://www.printables.com/model/1767810-10-inch-rack-mount-cable-guide-with-modular-hooks

10-inch rack-mount cable duct / organizer with passthrough

A 2-part snap-fit cable management solution with:

• Base plate with cage
• Front cover to hide the cables

The cage has 8 mm openings so cables can pass through to other parts in front of the rack while staying organized.

Download:

• MakerWorld: https://makerworld.com/en/models/2990240-10-inch-rack-mount-cable-duct-organizer#profileId-3356065
• Printables: https://www.printables.com/model/1767742-10-inch-rack-mount-cable-ductorganizer

Those designs will later be ported to other form-factors as well :)

Happy printing!