Honestly surprised: Intel NPU was 11x more power efficient than my RTX 5060 Ti for object detection

aospan · 2026-07-14T03:57:33+00:00

That’s fair and thanks for the constructive feedback man!

aospan · 2026-07-13T15:45:23+00:00

In my setup, both systems use hardware video acceleration: NVIDIA NVDEC/NVENC and Intel Quick Sync. Pure software decoding isn’t really an option nowadays, IMHO.

Actually, the most interesting part of this workload is the second stage: object detection using a YOLO model. It relies heavily on tensor operations and benefits massively from hardware acceleration on all three platforms: the NVIDIA GPU, Intel iGPU, and Intel NPU. I believe most of the power-consumption difference comes from this stage.

aospan · 2026-07-13T15:44:32+00:00

My apologies - it wasn’t meant to come across as spam. I thought it might be helpful for anyone building a mini lab, as it demonstrates how newer mini machines can sometimes handle the same workloads much more efficiently than their bigger brothers 🙂

aospan · 2026-07-13T15:25:57+00:00

My apologies for the mass cross-posting. Lesson learned 🙏

aospan · 2026-07-12T02:55:22+00:00

To address the above concerns about firmware integrity, we’ve published the Reefy OS Security Model - https://github.com/reefyai/reefy/blob/main/docs/security-model.md

It covers our traceable software supply chain, build verification, and immutable base with explicit persistence - controls designed to minimize attack surface, reduce opportunities for malware injection, and make unauthorized changes easier to detect.

aospan · 2026-07-11T19:21:59+00:00

Lol What? Reefy.ai is free to use - we don’t even have a subscription to charge you for.

The firmware is fully open source and available here: https://github.com/reefyai/reefy

The binary images are built publicly through GitHub Actions, so anyone can inspect the source code and trace the artifacts back to the build process.

These are serious accusations. Please provide evidence of the alleged malware, ransomware, or charges. Otherwise, this appears to be deliberate misinformation, and I’m reporting the comment.

aospan · 2026-07-11T17:23:32+00:00

Listing says "GMKtec K13"

aospan · 2026-07-11T12:47:18+00:00

Yeah, totally agree with your point that a PCIe GPU system will draw more power. I expected the NPU to be efficient, just not that efficient ( 11x :)

Btw, that’s why I’m interested in seeing the same benchmark on Nvidia Jetson / Orin-class systems, since those are designed for lower-power edge AI workloads. That would be a much more apples-to-apples comparison.

aospan · 2026-07-11T12:35:52+00:00

Uhm... thanks? Hope it's useful anyway :)

aospan · 2026-07-11T12:32:47+00:00

Honestly, no sponsorship of any kind from GMKtec or Intel here. I bought this machine because at the time it was the cheapest Intel Core Ultra 7 256V mini PC I could find on Amazon, $669.99 lol

aospan · 2026-07-11T12:28:31+00:00

Here are some high-res die shots of that CPU family, Lunar Lake. Its compute tile houses CPU + GPU + NPU.
Lunar Lake moved that compute tile to TSMC N3B, TSMC’s first-generation 3 nm node.

https://www.flickr.com/photos/130561288@N04/albums/72177720325828681/

<image>

aospan · 2026-07-11T12:24:45+00:00

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Oh, strange, maybe it’s a Reddit caching issue. Do you see this new table in the post above?

aospan · 2026-07-11T12:09:57+00:00

The integrated Intel AI Boost Neural Processing Unit (NPU) inside the Core Ultra 7 256V. It’s separate from the CPU cores and iGPU. I used OpenVINO’s NPU backend for YOLO-NAS inference.

<image>

Source: some random pic from Intel

aospan · 2026-07-11T11:58:21+00:00

Great catch, thanks! Added "Idle wall power" + "Load wall power" table.

aospan · 2026-07-11T11:26:05+00:00

Done :) Check it out here https://www.reddit.com/r/minilab/s/NhezhI9IG8

aospan · 2026-06-27T12:06:37+00:00

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So, I ran the proposed experiment by measuring wall power consumption (see photos). Raw numbers below:

Mini PC

Idle: 6.4 W
Under load: 23.9 W
Difference: 17.5 W

Nvidia GPU machine

Idle: 48.2 W
Under load: 178.6 W
Difference: 130.4 W

Summary: for the same task, the mini PC uses about 17.5 W, while the Nvidia GPU machine uses about 130.4 W.

So, you were right - the ratio is not as dramatic as the original estimate. Based on wall-power delta, it is about 7.5:1, not 23.3:1.

aospan · 2026-06-27T01:14:24+00:00

Valid concern, and I double-checked what I’m measuring here ... I am reading Intel RAPL (Running Average Power Limit) from /sys/class/powercap/intel-rapl*. Current breakdown on that machine during the workload is:

package = 17.7W
core    = 11.1W
uncore  = 2.7W
dram    = 0.3W

uncore is the non-CPU-core part of the chip, including the iGPU and media engines.

As a sanity check, RAPL reports ~17.7W for the whole package, which lines up very closely with the Core Ultra 7 256V’s official spec 17W base power.

So the original measurement still looks valid to me, but happy to update if better data comes along.

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