u/Porespellar

From the Digg article (https://digg.com/tech/3pf3046j)

“Google Gemma posted that the family of open models has achieved 1 billion downloads. The account is hosting an exclusive evening in San Francisco on August 20 to honor open-source builders, researchers, and contributors. Space at the event is limited and applications are required for attendance. Demis Hassabis shared the announcement from the official account. Clément Farabet replied that he will attend and is excited to celebrate the milestone. The post frames the achievement as driven by the community advancing the Gemmaverse forward.”

Seems like it would be the perfect venue to announce a new model, but could just be wishful thinking on my part

Thoughts? Speculation? Too busy testing Qwen 3.8 27b to care?

Would be a good time for Google to drop a 120b. Pull a Meta Glimmer and be king of the hill for a week or so before Qwen countered with a new mid-size model.

Having a ‘Killer Application’ that everyone wants to use helps sell hardware, plain and simple. DeepSeek V4 Flash 0731 isn’t an app of course, but I think it’s going to be the major catalyst for getting a lot of people to buy a couple of NVIDIA GB10-based systems because:

1. It is an amazing coding / agentic use model.

2. It fits perfectly on a 2x Spark Cluster

3. It runs Fast AF with the right vLLM recipe. (I’m getting 60 tk/s with this one:

4. https://github.com/tonyd2wild/DeepSeek-v4-Flash-0731-DSpark-1M-NVFP4-KV-2x-DGX-Spark)

5. You can run it with a fairly usable 1M context window.

6. It runs very well in harnesses such as

7. Hermes.

Now that solid NVFP4 support is finally here for DGX and is providing Sparks with a pretty good boost for token speeds, the Spark’s memory bandwidth limitation isn’t as big a deal as it used to be. I mean seriously, do I really give a shit about memory bandwidth when I’m getting 60 tk/s with Deepseek V4 Flash?

I know the Strix / M4 / M5 gangs may have something to say about all this, but even they have to admit that DGX Spark beats them for prompt processing performance, which is hugely important when it comes to agentic work and how fast agents are getting work done.

The Strix our-stuff-is-way-cheaper argument used to be very valid, but with memory and SSD prices being what they are now, that argument isn’t as strong as it once was. M5 stuff is pretty expensive and we have no idea when Apple is going to drop a new beefy Mac Studio M5 or a Mac Mini Pro with M5. We thought it was going to happen in June but they don’t appear to be in a rush to release anything.

So what’s left out in the market worth getting? Well, you could grab a RTX Pro 6000 if you want to pay a hefty premium from the scalpers, or you could try some of the AMD offerings, but other than that, the DGX Spark is still the best bang for your buck for getting the most VRAM to run models locally.

I didn’t even mention the low power consumption of the Spark which is another reason to consider it, especially with rising power prices.

I’ve noticed some price increases on Sparks and Spark clones from some retailers in the last few weeks. The 1TB Asus models seem to be the cheapest options out there that I’ve seen.

I think we’re going to see Spark scarcity in the market very soon as word gets out about how well DeepSeek V4 Flash runs on it.

I’m running a 2x cluster and i’ll say that for the first 6 months or so, I, like many other folks, was disappointed with the software support and the speed of the models I tried. Ever since they finally resolved the NVFP4 Issues, and since DSpark, MTP, Prism, DFlash, and other performance improvements have been implemented, it’s gotten A TON better and I’m honestly thinking of buying another 2 Sparks if I could find the money to get a couple more. Deepseek V4 Flash 0731 absolutely smokes on my cluster and I have 0% buyers remorse now, where I would have said it was maybe 50% just a few months ago.

Do y’all agree or disagree? Also, no shade intended for the Strix and M5 gangs. Would love to hear how well DeepSeek V4 Flash is working for you guys as well.

My kid had an old pixel art display (Divoom 32x32 Pixoo-max) that they weren’t using anymore, so I thought it might be fun to repurpose it as a GPU cluster status monitor so I can see GPU temps / utilization / token gen info etc for the 3 RTX A6000s in my vLLM Ray cluster (currently running Qwen3.5 122b).

I spun up my Hermes Agent (GLM 5.2 as the agent model) and told it:
“I would like you to build an application that will run on <computer name of my Dell GB10> that will display GPU cluster health data on a 32x32 pixel Divoom Pixoo-max display that can be connected to via Bluetooth. You should probably read the following repos to learn about the pixel display and how to connect to it:
- https://github.com/SomethingWithComputers/pixoo
- https://github.com/cyanheads/pixoo-toolkit
- https://divoom.com/products/divoom-pixoo-max
The app should display system health data for the 3 systems in my vLLM Ray cluster in an easy to read and understand manner. It should also show similar data for the Dell GB10 (in the network segment but not in the cluster). This could be as simple as showing 4 boxes on the screen that show the cluster system’s initials such as “S1” and have a background color to indicate GPU temperature (red for hot, green for normal, etc). The 32x32 screen size limit will make it difficult to show a lot of information so you’ll have to be creative in how you display it, you can also cycle through multiple screens of different metrics in 4 second intervals. “

For those who care:
HW:
- 3x Dell Precision 7960 workstations each with an RTX A6000 GPU (64GB RAM) currently hosting Qwen3.5 122b
- 1x Dell Pro Max GB10 (not part of the Ray vLLM cluster but runs the app thar is cast to the display as well as running a secondary LLM endpoint for other models. The GB10 has the Bluetooth radio in it that is used to connect to the Divoom. The Dell towers don’t have Bluetooth which is why I used the GB10.
- Divoom Pixoo-max 32x32 pixel display. They also make a 64x64 pixel version as well. It was around $60 when I bought it years ago.

It took GLM 5.2 all of like 20 minutes to build this, and maybe another 5 minutes of me working with it to get it how I wanted it. It’s not perfect, but it’s cool to be able to visually glance over at the cluster and see what’s happening without logging in, and it really didn’t cost anything since I already had the pixel display that would have been headed for the thrift bin.

Btw, Hermes / GLM did the whole thing in Python, from Ray Dashboard API, vLLM metics endpoint, and Nvidia-smi calls over ssh.

I mean seriously y’all, what an amazing past few days. So many awesome new models to test out in the mid range model sizes.

EDIT: Added some of the more interesting models that came out over the last week and a half

- Thinking Machines Inkling Small
- DeepSeek v4 Flash 0731
- Poolside Laguna
- Upstage Solar
- Microsoft Mage VL
- LG ExaOne
- SenseNova u1.5
- BottleCap - ThinkingCap
- Kimi K3

/s

TL:DR: I’m a grad student in AI, I saw that Google released TabFM and TimesFM last week, I built an MCP wrapper to serve both transformer models in a single Docker container so you can connect their new ML transformer models to a local LLM via Open WebUI, Claude Code, or Codex and do ML tasks that would have previously required building, training, and tuning ML models to do. Tested with classic ML datasets (Iris, California Housing, etc), Pretty solid scores for accuracy for being zero-shot: (94.7% for Iris) and R2 of 0.91 for regression test) vs. traditionally tuned ML models. You need about 16GB of VRAM to run both models. I added dynamic model load and unload with a TTL set to 5 mins. CSV. support now, with XLS, XLSX, JSON, JSONL support soon. PyTorch-based so CUDA only. Works on DGX Spark, 3090, H100 and most anything Nvidia with 16GB+ VRAM. Install script auto detects architecture.

Here is my repo if you want to try out the MCP:

https://github.com/porespellar/Zer0Fit

Here’s the non-TLDR version:

I’m working on my Masters in AI and I saw someone’s post here the other day about Google’s new TabFM Tabular data foundational transformer models released last week and I thought that they were super groundbreaking in that they were basically bringing ML models into the GenAI space which is both weird and cool because ML models are very different animals than LLMs

Here was the original Google blog post on it:

https://research.google/blog/introducing-tabfm-a-zero-shot-foundation-model-for-tabular-data/

Anyways, I wanted to play around with these new models from a chat interface and try to “kick the tires” a bit, so I built an MCP implementation for both the TabFM and TimesFM models. Nothing super fancy, just a quick and dirty MCP wrapper of the PyTorch versions (this will only run on CUDA).

I made the MCP with 2 build targets in mind: DGX Spark (arm-based with CUDA 13) and 3090 (AMD64 with CUDA 12.6). No Mac support because of Google using PyTorch, sorry.

I also wanted this to work with my preferred chat client: Open WebUI, so that’s what it’s geared towards running best with and was tested against, I also added Claude Code and Codex CLI support as well, but haven’t really fully tested those out yet.

Install is just a git clone and an ./install.sh. The whole thing runs out of a single Docker container and dynamically loads and unloads the models into VRAM with a TTL of 5 minutes to free up reserved VRAM when not in use. I also included an Open WebUI Skill.md that can be imported into Open WebUI, and skill.md and agents.md for the other harnesses.

I tested it with some fairly classic ML datasets from Kaggle that most data science students have probably encountered while studying AI/ML.

- Iris (classifiers)
- California housing (regression)
- Airline Passengers (time series forecast)

I spent a semester trying to learn ML models and tuning them and not really knowing what the hell I was doing, usually overfitting my models, and changing all kinds of parameters that I didn’t know if they were really helping or hurting my models. It all seemed like a dark art that I never fully understood. TBH, I wasn’t really a fan of ML, I think it’s cool stuff, but I just don’t have the math skills or stats chops to be able to understand WTF I’m doing most of the time with hyperparameters tuning. A man has to know his limitations, LOL.

Anyways, as I said earlier, I just wanted to get Google’s cool new ML models running where I could feed a dataset to an MCP and then have it do all the ML magic that Google trained these foundational models to do. I tried to make it easy for the average person like myself to run. I thought others might want to test out the models too so I made it a public repo.

So here it is if you want to mess around with it:

https://github.com/porespellar/Zer0Fit

I’ll try and do some maintaining if I see that there is any continued interest, but I can’t promise that I’ll keep up with it, so please feel free to fork the repo and take it in any direction you want to.

I think models like TabFM and TimesFM are going to low-key bring the branches of AI / ML tree closer together and we’re going to see some really cool and wild stuff as people take these concepts further in the future.

Note: This repo was hastily built to just get the models running. I’ve done very limited testing only on DGX Spark. Again, feel free to fork it and make it as good as you want to.

And please remember that this stuff is very experimental. Don’t use the forecasts or predictions made by these models for anything other than just research curiosity. Use at your own risk.

Let me know what you think of the repo if you give it a try. Cheers.

Note Regarding my test results in the images: I created the test scripts using DeepSeek V4 Flash and I had Claude Opus 4.6 review the test methods, code, and results. I don’t claim to be smart enough to know if the stats / math is correct. I would love it if some of the very smart ML research folks on here would give the repo a try and let us know if they are getting similar results or if my results are completely wrong. I included the sample datasets in the repo so “apples-to apples” comparison tests could be run by others to either prove or disprove my results. I really don’t mind if I’m wrong, I’m a student and just want to learn and improve.

TL:DR: I’m a grad student in AI. I saw that Google released TabFM and TimesFM last week. I built an MCP wrapper to serve both transformer models in a single Docker container so you can connect their new ML transformer models to a local LLM via Open WebUI, Claude Code, or Codex and do ML tasks that would have previously required building, training, and tuning ML models to do. Tested with classic ML datasets (Iris, California Housing, etc), Pretty solid scores for accuracy for being zero-shot: (94.7% for Iris and R2 of 0.87 for regression test) vs. traditionally tuned ML models. You need about 16GB of VRAM to run both models. I added dynamic model load and unload with a TTL set to 5 mins. CSV. support now, with XLS, XLSX, JSON, JSONL support soon. PyTorch-based so CUDA only. Works on DGX Spark, 3090, H100 and most anything Nvidia with 16GB+ VRAM. Install script auto detects architecture.

Here is my repo if you want to try out the MCP:

https://github.com/porespellar/Zer0Fit

Here’s the non-TLDR version:

I’m working on my Masters in AI and I saw someone’s post here the other day about Google’s new TabFM Tabular data foundational transformer models released last week and I thought that they were super groundbreaking in that they were basically bringing ML models into the GenAI space which is both weird and cool because ML models are very different animals than LLMs

Here was the original Google blog post on it:

https://research.google/blog/introducing-tabfm-a-zero-shot-foundation-model-for-tabular-data/

Anyways, I wanted to play around with these new models from a chat interface and try to “kick the tires” a bit, so I built an MCP implementation for both the TabFM and TimesFM models. Nothing super fancy, just a quick and dirty MCP wrapper of the PyTorch versions (this will only run on CUDA).

I made the MCP with 2 build targets in mind: DGX Spark (arm-based with CUDA 13) and 3090 (AMD64 with CUDA 12.6). No Mac support because of Google using PyTorch, sorry.

I also wanted this to work with my preferred chat client: Open WebUI, so that’s what it’s geared towards running best with and was tested against, I also added Claude Code and Codex CLI support as well, but haven’t really fully tested those out yet.

Install is just a git clone and an ./install.sh. The whole thing runs out of a single Docker container and dynamically loads and unloads the models into VRAM with a TTL of 5 minutes to free up reserved VRAM when not in use. I also included an Open WebUI Skill.md that can be imported into Open WebUI, and skill.md and agents.md for the other harnesses.

I tested it with some fairly classic ML datasets from Kaggle that most data science students have probably encountered while studying AI/ML. Included the datasets in the repo as examples.

- Iris (classifiers)
- California housing (regression)
- Airline Passengers (time series forecast)

I don’t know how many folks on here really care about ML regressors, classifiers, or forecasters (time series), but I spent a semester trying to learn about them and tuning them and not really knowing what the hell I was doing, usually overfitting my models, and changing all kinds of parameters that I didn’t know if they were really helping or hurting my models. It all seemed like a dark art that I never fully understood. TBH, I wasn’t really a fan of ML, I think it’s cool stuff, but I just don’t have the math skills or stats chops to be able to understand WTF I’m doing most of the time with hyperparameter tuning. A man has to know his limitations, LOL.

Anyways, as I said earlier, I just wanted to get Google’s cool new models running where I could feed a dataset to an MCP and then have it do all the ML magic that Google trained these foundational models to do. I tried to make it easy for the average person like myself to run. I thought others might want to test out the models too, so I made it a public repo.

So here it is if you want to mess around with it:

https://github.com/porespellar/Zer0Fit

I’ll try and do some maintaining if I see that there is any continued interest, but I can’t promise that I’ll keep up with it, so please feel free to fork the repo and take it in any direction you want to.

I think models like TabFM and TimesFM are going to low-key bring the branches of AI / ML tree together and we’re going to see some really cool and wild stuff as people take these concepts further in the future.

Note: This repo was hastily built to just get the models running. I’ve done very limited testing only on DGX Spark. Again, feel free to fork it and make it as good as you want to.

And please remember that this stuff is very experimental. Don’t use the forecasts or predictions made by these models for anything other than just research curiosity. Use at your own risk.

Let me know what you think of the repo if you give it a try. Cheers.

Note Regarding my test results in the images: I created the test scripts using DeepSeek V4 Flash and I had Claude Opus 4.6 review the test methods, code, and results. I don’t claim to be smart enough to know if the stats / math is correct. I would love it if some of the very smart ML research folks on here would give the repo a try and let us know if they are getting similar results or if my results are completely wrong. I included the sample datasets in the repo so “apples-to apples” comparison tests could be run by others to either prove or disprove my results. I really don’t mind if I’m wrong, I’m a student and just want to learn and improve.

I’m pretty jaded like most of y’all. I don’t really get excited by new models much anymore. Last few weeks have been kinda meh to be honest. Monday, I stumbled upon SenseNova’s Mixture of Transformers models and they seem kinda like a different animal than other typical image gen models.
I managed to get a couple of them running and I have to say that this series of models is impressing me when it comes to generating and editing dense infographics.

I haven’t seen anything except for Ideogram 4 get close to what these can make in terms of infographics. While Ideogram 4 is great, Ideogram’s license sucks, SenseNova is Apache 2, so that puts them over the top when going head-to-head in my book.

Now I know, I know, the latest SenseNova-u1 version 2 is not in GGUF form yet, but that’s not a problem. What I did and what you can do is tell your favorite coding harness to “take the SenseNova model and wrap it in a FastAPI wrapper and serve it as both an OpenAi-compatible image generation endpoint and a image editing endpoint in a single docker container” and let that cook for a while and boom, Bob’s your uncle. In a bit you’ll have you an image generation API endpoint that you can point your favorite chat client to as an image generator / editor. This will let you skip all that ComfyUI spaghetti-looking interface bullshit. I’ve never been a fan of ComfyUI and don’t think I ever will. Change my mind.

There are several different versions of the SendeNova U1 models that you can try. If you want to.

Infographic V2 just came out a couple days ago and is the 50 Step base model. By the way it can make pretty much any image, it’s just trained to do infographics really well.

https://huggingface.co/sensenova/SenseNova-U1-8B-MoT-Infographic-V2

Infographic V1 8 Step LORA is like a lower-quality “flash” type model merge that is super speedy but not as high quality obviously because 8 steps is less than 50 (duh).

https://huggingface.co/sensenova/SenseNova-U1-8B-MoT-LoRAs/blob/main/SenseNova-U1-8B-MoT-Infographic-LoRA-8step-V1.0.safetensors

Infographic V1 50 Step base is also available but there is no reason to use it anymore unless you want to use it with the 8 Step LoRA for high speed generation.

https://huggingface.co/sensenova/SenseNova-U1-8B-MoT-Infographic

They also recently released an “Interleaved images” model which is really interesting.

https://huggingface.co/sensenova/SenseNova-U1-8B-MoT-Interleaved

The interleaved version will let you generate a series of related images, with consistent characters, fonts, colors, etc. Use cases for it include making slide decks with a consistent theme, making story books, etc. You have to serve the interleaved version differently because multiple images is not something a standard OpenAI-compatible Image generator endpoint can handle yet, so you need to create a tool pipeline with emitter events to serve multiple images in a single chat. I’m sure your harness can figure out how to set it up for you, mine did.

Anyway I thought these models were interesting and fun to get running. You’ll probably need about 36 GB of VRAM for the full bf16, but there are some quants and different GGUFs available as well. I think the smallest one I saw needed like 16GB.

Mostly /s but,

I mean….. I’m no CEO…. but it seems like this would be the absolute perfect time to drop a super powerful GPT-OSS-2 to throw a big ol’ wet blanket on Anthropic’s IPO. It doesn’t need to be like frontier or anything, just a 20b and a 120b that is as fast as the old versions, add agentic coding focus, and maybe vision capabilities. It would fill the void left by Qwen in the 120b size category and maybe would push Google to release their 120b that they yanked during the Gemma 4 launch.

I’ve been on the waitlist since last Wednesday and I tried a bunch of so-called hacks to try and push the phone to get off the waitlist, I know that they were all BS, however, I’ve been checking the Apple Intelligence storage size periodically and it went from 6.47GB to 21.84GB this morning. Wondering if this is it staging the files for the new Siri update, or just a result of me switching back and forth to different languages when I was trying to trigger a Siri update through one of the bogus hacks.
For those of you who already have the new Siri update, could you tell me what your “Apple Intelligence” storage size says in Settings > General > IPhone Storage > iOS?

If you’re trying to download and preserve your local LLMs in case of future availability issues due to AI-related politics, your best bet is either 128gb or 100gb Blu-Ray optical disks, more specifically BD-R XL M-DISC standard format which are archival-grade and built to last for like 10 of our lifetimes.

And yes, cheap USB thumb drives are the other option, but they are considered volatile storage and could be affected by static discharge and other electrical issues.

So if you’re worried about preserving your favorite models long term, maybe pick up a Blu-Ray burner. You can get them for around $100 -$250. Blank Blu-Ray disk prices for 100gb to 128gb disks vary wildly depending on quantity and quality. 128gb average around $12-$14 per disk. 100gb can be found for about $7 -$10.

There hasn’t been a huge demand for the blank disks until recently because hard drive and memory prices used to be much lower. Given this fact, expect low stock on the blanks for a while most likely. Hopefully companies will ramp up production of the blank disks as demand from data hoarding folks like us increases.

The Best commonly available BD-R XL capable burner compatible with high capacity M-DISK that I’ve found so far is the:

ASUS 16D1X-U
https://www.asus.com/us/motherboards-components/optical-drives/external-blu-ray-drive/bw-16d1x-u/

But there are tons of other great drives out there from Buffalo, LG, and others for as cheap as $80 for a lower-speed external drive.

As far as the blank media goes, look for the 128gb and 100gb blank BD-R XL disks from Verbatim and Ritek, expect to pay a premium for the M-DISC version that is built to last longer than the standard version. M-DISC is not a must have, but it’s the highest archival quality version available to consumers right now.

It sucks that current world events have driven us into becoming AI model archivists, but if we don’t do it, then I don’t know who else will. The best LLM is the one you have access to when the shit hits the fan. LFG back up some models!

First Doom, now DiffusionGwmma 4. We are truly living in the future. Who even needs a new Qwen release anymore? /s
(Satire - Shaq doesn’t actually make a digital pregnancy test capable of running diffusion-based LLMs)
Credit to Obvious Plant for the original Shaq pregnancy test box (that I doctored slightly).

For real tho, these bots need to turn on their web search functions and quit living in the past. It’s bad enough we gotta deal with all the “Qwen3.6 27b helped me quit drinking and brought my dog back from the dead” posts. Sheesh /s

Of course I’m thankful for all that Qwen has bequeathed us, but deep down in the darkest pit of our souls, every last one of us are just all sitting here waiting for Qwen to say “Hey Google, hold my beer while I drop the best GD model of all time on these fools” /s

Unfortunately, the steam-powered GPUs of the era were incapable of anything higher than a 4K context limit.

I’ve found that DGX Sparks can get pretty warm when you cluster them together. You are forced to keep them close together because the ConnectX-7 cable made for these is extremely short )like less than a foot).

I have both a DGX Spark Founder’s Edition and a GIGABYTE AI TOP Atom (Spark clone).I decided I wanted to add some active cooling to the cluster so I found cooling case plans for a 2 Spark fan case that someone has posted to Thingverse:

https://www.thingiverse.com/thing:7355793

A friend of mine 3D printed it for me in PETG filament which he said was better for higher temperature applications than standard PLA.

The cooling enclosure has space for 2 Sparks (or Spark clones) plus a removable shelf in the middle that leaves a gap between them for air flow. The front has space for a 120mm x 120mm x 25mm fan. It also has two retention rods that slide into place to keep the Sparks from sliding out the back of the enclosure.

I wanted the cooling fan to be automatically thermostat-controlled so I bought an AC INFINITY fan controller that has a temperature probe. This controller is normally used for adding cooling to home theater rack enclosures or grow boxes for “hydroponics” (wink, wink), but I thought it should work of in this application as well.

AC Infinity Controller 2:
https://www.amazon.com/dp/B00NG9TSG4?ref=ppx\_pop\_mob\_ap\_share

I can set a maximum temp that will trigger the fan to come on, and the unit will adjust the fan speed as needed based on the probe temperature feedback.

I chose an AC Infinity MULTIFAN S3 USB fan (https://www.amazon.com/dp/B00G05A2MU?ref=ppx\_pop\_mob\_ap\_share) because it was made to pair well with the fan controller of the same brand.

I’ll probably build a separate enclosure for the fan controller as it has mounting holes and is meant to be recessed mounted into furniture.

I literally just finished the build this morning, so I haven’t run any performance tests on it yet but I will definitely do that at some point soon if anyone expresses interest in knowing that kind of information

The fan controller was $50, the fan was $15, and my friend said the 3D print consumed about 3/4 of a $20 spool of PETG filament. So about $80 for all the parts.

One question I had for all the cooling gearheads out there: Right now, I have the fan pointed in the direction where it’s pulling air from front of case and blowing it through the Sparks towards the back. Is that the proper direction for the fan orientation for this situation or should I have it the other way around?

I’ve found that DGX Sparks can get pretty warm when you cluster them together. You are forced to keep them close together because the ConnectX-7 cable made for these is extremely short )like less than a foot).

I have both a DGX Spark Founder’s Edition and a GIGABYTE AI TOP Atom (Spark clone).I decided I wanted to add some active cooling to the cluster so I found cooling case plans for a 2 Spark fan case that someone has posted to Thingverse:

https://www.thingiverse.com/thing:7355793

A friend of mine 3D printed it for me in PETG filament which he said was better for higher temperature applications than standard PLA.

The cooling enclosure has space for 2 Sparks (or Spark clones) plus a removable shelf in the middle that leaves a gap between them for air flow. The front has space for a 120mm x 120mm x 25mm fan. It also has two retention rods that slide into place to keep the Sparks from sliding out the back of the enclosure.

I wanted the cooling fan to be automatically thermostat-controlled so I bought an AC INFINITY fan controller that has a temperature probe. This controller is normally used for adding cooling to home theater rack enclosures or grow boxes for “hydroponics” (wink, wink), but I thought it should work of in this application as well.

AC Infinity Controller 2:
https://www.amazon.com/dp/B00NG9TSG4?ref=ppx\_pop\_mob\_ap\_share

I can set a maximum temp that will trigger the fan to come on, and the unit will adjust the fan speed as needed based on the probe temperature feedback.

I chose an AC Infinity MULTIFAN S3 USB fan (https://www.amazon.com/dp/B00G05A2MU?ref=ppx\_pop\_mob\_ap\_share) because it was made to pair well with the fan controller of the same brand.

I’ll probably build a separate enclosure for the fan controller as it has mounting holes and is meant to be recessed mounted into furniture.

I literally just finished the build this morning, so I haven’t run any performance tests on it yet but I will definitely do that at some point soon if anyone expresses interest in knowing that kind of information

The fan controller was $50, the fan was $15, and my friend said the 3D print consumed about 3/4 of a $20 spool of PETG filament. So about $80 for all the parts.

One question I had for all the cooling gearheads out there: Right now, I have the fan pointed in the direction where it’s pulling air from front of case and blowing it through the Sparks towards the back. Is that the proper direction for the fan orientation for this situation or should I have it the other way around?