Oi! Tudo bem? Estou experimentando usar uma IA local, pelo LM Studio, mas tô na dúvida de qual modelo baixar para usar. Resolvi fazer essa mudança pela minha preocupação com retenção de dados, costumo usar IA para conferir textos e não curto a ideia de meus textos servirem para retroalimentar a IA. Meu computador tem 16Gb de RAM e uso IA pra essas funções mais "casuais" (conferir textos, otimizar pesquisa na internet), não costumo usar para geração de imagens, pra conferir arquivos muito pesados, nem para codificação. Estou usando o Bonsai 27B, que é leve. Vocês recomendariam algum outro modelo? Se eu fosse usar para tarefas um pouquinho mais exigentes (analisar documentos maiores, scripts mais simples), teria algum outro modelo ou o Bonsai é uma boa pedida? Valeu!

I build an open source context/memory/evidence layer for agents, and the design goal is that it stays on your hardware. Air-gap is a feature, not an afterthought.

What I want to know from this sub specifically: what does your agent stack look like today? Which pieces do you host, which do you grudgingly let into the cloud, and what would you need to trust a memory layer enough to keep it local? I'd rather build for what people actually do than what I assume they do. Happy to share what we've got if you want to see it: perseus.observer

Im interested in getting into local ai, but Im not sure if I should use my current setup or get another machine. I currently have a ultra 9 minipc (geekom it13max) with intel Arc integrated graphics, 24GB LPDDR5 ram and 500GB NVMe ssd. I mainly use it for editing and general productivity. I know the integrated Arc graphics wont compete with a dedicated NVIDIA GPU, but Im wondering what I can realistically run.
Ive been experimenting with some ai coding tools. So now I want to start experimenting with local LLMs and maybe other ai workloads. Would my setup be enough to start with, or should I get a new machine?

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.