I have one drive in this device, and it’s a real danger for NVME drives.

The worst part is that the only way to control the fan speed is either to turn it off completely or to set it to jet engine speeds!

I don’t know who in this group works with the engineers at Ugreen, but someone should tell them to add a manual fan curve adjustment. Either pick good fan curves or let the people choose what they can tolerate but this is insane.

I could technically SSH into the terminal, poke around, and find knobs to fix this problem. However, I didn’t buy an all-in-one solution just to break the device’s configuration management.

If I have to do all that I might as well install unraid or truenas.

I would love to keep ugos because I really do like it but not at the cost of my 4TB Samsung drive lighting itself on fire in this economy.

Mind you these temps are at idle…. My house is at 72 degrees.

Early Impressions: UGREEN IDX6011 Pro NAS with AI Features

I received my UGREEN IDX6011 Pro on June 2 as one of my early preorder units from about a year ago. I wanted to share some early thoughts after getting it set up and doing some initial AI testing.

For context, I have a background in electrical engineering, a Master’s in Computer Science, and I currently work as a software engineer focused on AI tools. My main interest here is seeing how well UGREEN can integrate local AI into a home NAS, especially given the compute limits of this type of hardware.

Setup So Far

So far, I have:

• Installed a Samsung 990 Pro 4TB NVMe SSD

• Created my shared folders on the new volume

• Migrated data manually using Syncthing

• Launched Docker containers for migration

• Used Portainer to bring up stacks from my previous unit

• Adjusted most settings to match my old configuration

The hardware itself feels solid. Compared to my 6800 Pro, the IDX6011 Pro feels heavier, sturdier, and more finished overall.

One big improvement is thermals. My older 6800 Pro overheated often and would thermal throttle under load. So far, the IDX6011 Pro has not shown that same behavior.

The one physical issue I ran into was the side panel. It did not go on evenly or cleanly, and I had to use more force than expected to get it installed. I am not sure if there is a trick to the assembly, if my panel is slightly warped, or if it was just user error.

The NVMe area is also a concern. It appears to have essentially no direct cooling, and my 990 Pro is already running a bit warm. That feels like a poor design choice for a NAS, especially if users add an expansion card that does not have its own fan.

Initial AI Testing

For this review, I used the built-in speech-to-text feature directly on the unit, then refined the transcript using the built-in Qwen 3.5 35B model, which has 3 billion active parameters.

The model was faster than I expected. During transcript refinement, I was seeing roughly 20 tokens per second, which is pretty impressive for this kind of device.

That said, the AI experience still feels early.

The model had a hard time keeping track of conversation length and history, even with only six total short messages. It also does not seem to have reliable time awareness and generated random times in responses.

The tooling is also rough right now. I had to “massage” prompts and inputs to get the LLM to perform specific actions. It works, but it does not feel smooth yet.

Issues I Ran Into

Here are the main issues I have noticed so far:

• Side panel fit was awkward and required force to install

• NVMe area has essentially zero direct cooling

• NVMe drives run a bit warm

• Expansion cards without their own fans may have thermal issues

• AI model struggles with short conversation history

• Model lacks reliable time awareness

• Tooling feels janky and requires too much input manipulation

• Uliya app fails when trying to enable cloud models

• GPU acceleration reports as unavailable

• OpenAI keys and OpenAI-compatible endpoints show authentication errors

• The system may say authentication succeeded, but still blocks model selection

• No fan curve control

• No persistent LLM memory across sessions

• No obvious way to pass through external GPU devices to virtual machines

The biggest concern for me is compute transparency.

I suspect some of the AI workload may be offloaded to the host system running the UGREEN app instead of being processed entirely on the NAS. I am not saying that is definitely happening, but UGREEN needs to make this clear. If the architecture is hybrid, that is fine, but it should be stated clearly.

What I Like So Far

The hardware feels genuinely good. The unit is sturdy, the build quality feels more polished than the 6800 Pro, and the UI fit and finish are excellent so far.

The overall system cooling also seems much better than the 6800 Pro. My 6800 Pro overheated and thermal throttled often, while the IDX6011 Pro has not done that so far in my testing.

The built-in Qwen model also performs better than I expected in terms of speed. Seeing around 20 tokens per second during refinement was a pleasant surprise.

The Photos app implementation is also almost on par with competitors like Immich.

What I Want UGREEN to Improve

UGREEN needs to give users more model flexibility. I do not want to be locked into a single preloaded model. Ideally, the system should allow users to download and run models of their choice.

I also wish UGREEN had gone with the newer Qwen 3.6 35B-A3 model instead of the current Qwen 3.5 35B model.

UGREEN should also unify the app ecosystem. I do not want separate ecosystems for IDX units and standard units. Standard UGREEN NAS units should still be able to use API keys or connect to a local OpenAI-compatible server, especially for users who already run their own home LLM server.

They also need to add something like Honcho DB for persistent memory across sessions. If the NAS is going to have an AI assistant, it should be able to remember useful context between sessions instead of acting stateless every time.

UGREEN should also add fan curve controls. Power users need more control over cooling, noise, and sustained performance.

The NVMe and expansion-card area also needs better cooling. Right now, that section appears to have little to no direct airflow. That is not ideal for sustained NAS workloads, cache drives, indexing, containers, or AI-related tasks.

I would also like to see external GPU passthrough support for virtual machines. If the system supports advanced workloads, users should be able to pass through external GPU devices where the hardware allows it.

At minimum, I would like to see:

• Better model selection

• Support for local OpenAI-compatible LLM clients

• Working external API key support

• Clearer disclosure of where AI inference is happening

• Fixes for Uliya cloud model settings

• Fixes for time awareness in the system prompt

• Fixes for GPU acceleration reporting

• Better conversation memory and context handling

• Persistent LLM memory across sessions

• Fan curve controls

• Better NVMe and expansion-card cooling

• External GPU passthrough support for virtual machines

Overall Early Take

My early impression is that the IDX6011 Pro has strong hardware, a polished UI, better system thermals than the 6800 Pro, and surprisingly fast local AI performance for a NAS. The Photos app implementation is almost on par with competitors like Immich.

But the AI software stack still feels very early. The model struggles with basic conversation tracking, lacks reliable time awareness, and requires too much input massaging to use smoothly.

The biggest open question is whether AI processing is truly happening on the NAS or whether some of it is being offloaded to the host machine. UGREEN needs to be transparent about that.

Overall, I think the hardware is promising, but the AI side needs more polish, more openness, persistent memory, better compatibility, better NVMe cooling, and more power-user controls before it fully delivers on the idea of a local AI NAS.

[Updated, needed to add a few extra notes]