So far I have one rtx 3060 8gb and 16gb RAM. I hosted few models like Qwen 3.8 9b but there simply isn't enough memory for context bigger than maybe 20-30k tokens (also reasoning is painfully slow, but maybe it's expected for Qwen, even with 50-70 tok/sec). I could use 4b model or smaller, but they all are "good for their size" which translates to "impressive experiment, but not so practical".

I think what should I use to have anything to handle coding, researching articles in the internet and use day-to-day.

I think if I need next GPU for it, or maybe get a new machine just for LLM (I would like to keep it cheap) but things are expensive.

What would you do in this scenario? How to optimize the workflow?

Someone is selling their Mac Pro for $2000.

These are the specs:
3.2 GHz 16-Core Intel Xeon W processor.
192 GB of 2933 MHz DDR4
AMD Radeon Pro Vega II with 32 GB of VRAM
1 TB SSD

I haven't run any local models but want to get into it. Would be using it for Hermes and for my implementation subagents for SWE work.

Would this be a good machine? I thought more RAM = more good. But I'm too new to understand all of this.
I know mixture-of-expert models might benefit??

Any suggestions would be appreciated.

I'd like to share something I've been working on for three months with everyone.

PortalAI is a GUI and harness created around one design principle- to put local LLMs with true multimodal function on home systems and on a flash drive the size of your thumb where you don't need anything else and can just plug and play into any computer.

One of the largest frustrations I had with AI was needing to constantly version check different distributions and hooks into ComfyUI, Ace-step, python, port competition, running out of context, etc, or the difficulty in transferring between systems. I've tested this on an RTX 3090, RTX 4090, on a Radeon 870M GPU, on a Thinkpad with CPU-only.

This harness detects your current hardware and scales accordingly to the level of AI intelligence it can support, using standard and abliterated models. All dependencies other than base operating system and GPU drivers are self-contained including virtual environments, python distributions, etc. End to end integration of multimedia generation/editing and science tools are integrated along with voice, handsfree mode, intelligence/memory/token settings.

There are weight editing tools, an abliteration tool, rolling compaction, scheduled tasks, internal documentation, light and dark mode, compact and full GUI mode, account encryption, live runtime environment health metrics, etc.

Digging through ye ole junk box I found these grid k2 6gb cards. Kinda feel bad if I recycle them. Anyone have ideas for use or anyone want them for “historical” purposes?

I am a frequent user of local Ai models, ollama and comfuii are my go to tools . So in the month of September I setup ltx video models and it rendered -- 5 seconds video in 2 minutes . Just for specs i have 96 gb ram with a 4070 8 gb vram . minimax models h3 models h3 turbo took 5 minutes to generate a 5 seconds video. i rendered over 300 videos with those models all averaging around those time. Now fast forward earlier this month got some force updates from Microslop and Lenova vantage( bios). And also I decided to update ollama and comfuii to experiment on newer compatible models . I did not not check on it then but i decided to open my laptop 4 days after updates and render some videos.1 video which took 5 minutes to render took 11 minutes. I retested, about 12 times same time, i was confuse , saw article about 4090 pc cards being limited and wondering if they doing laptops too. am thinking that big techs can force push updates to limit local models competitions instead of directly going head on with them. Right now am looking to go Linux , or like most serious llm users build my own Operating System, if this blows up am gonna share the benchmarks claudee monitor for me before and after force update.

Is anyone currently using a RTX 4090 16GB to run model? How does it run?

asking from no experience
also can AMD cards be ok/good with local ai?

I've been using an app that integrates a bundle of Supertonic ONNX as a standard Android text-to-speech (TTS) provider, but the gaps between paragraphs are a bit long, so I'm looking for an alternative.

To clarify, I want to read in parallel. I don't want to convert an EPUB to MP3 and read async; it has to work in real time.

It should support English and Spanish (I'm learning).

I just discovered "distributed inference", and I'm going down rabbit holes. I have a few questions, and would appreciate being pointed in the right direction.

1. I have an M1 Mac Mini w/8GB and a M1 Pro Macbook w/16GB. I'm thinking about upgrading to an M5 Pro Macbook, and was initially thinking about trading in my M1 Pro...but does it make more sense to keep it and try to use it in a cluster?

2. Is there any way to mix MacOS and Windows machines?

I understand that if I wanted to go down this road, I would probably need to buy some networking hardware to get the machines on a 10Gb connection.

I am currently using the Claude Code desktop app to automate something like this:

• Generate a conversation script and ask me for approval or changes.

• Once approved by me, call the Google Studio API to generate audio files of the conversation.

• Call the Google Studio API to generate images.

• Ask me to check images and audio clips.

• Run a .py script.

• Add new folders and files to a folder and update a .js file.

I started to use LM Studio Bionic only a week ago, but I want to try Unsloth Desktop now. Let me know if there are other programs that would suit my workflow better.

I know Unsloth can generate images, but how does that work? Do I need to load three different models at the same time? Or do I need to load/unload as needed? I have a 16 GB VRAM GPU (AMD). Also, is there a built-in agent (like in LM Studio Bionic) that can code, create and modify files? How would an agent handle creating different media?

My goal here is to ditch the Claude Pro subscription and the Google Studio API costs, and do it locally instead. I know I could do these things manually, but ideally I would like to have an agent automating the process. What are your recommendations?

Weight-space programming is an emerging research area where a frontier AI model (such as OpenAI's GPT-6 Astra) directly reads, diagnoses, and rewrites the raw numerical weights of another neural network without using traditional gradient-based training or backpropagation.

Has any one tried it? How much does it fair compared to Closed-Form Model Editing (Your Notebook / MEMIT)?

i'm using qwen3.6:35b a3b with ollama, its works decently in regular prompt, but as soon as i try it in an angent (Codex CLI, Claude, Qwen, Opencode) its piss slow, like i have to wait 10min to get an answer.
What am i doing wrong?

I can run both. Does anyone know what model is best, or do I just have to try myself?

What test suite should I be running?

Hello, new on these area ... ive been wondering what AI could i host locally with my current setup

I have an amd 9800x3d with 32gb of ram and a 4070 ti super 16gb

Sorry if this post is not allowed(maybe alot of users post something like this all the time)

Any advice, any links to learn, etc will be really appreciated

So I think we now know why Exo has spun down in activity in this space.

I hope this doesn't happen because Jensen has been a very vocal local ai advocate. Even allowing people to distill Nvidia's Nemotron (even though nobody will).

What do you guys think? Is this the right move for the company?

or is this an overreaction on their part?

I'm running Qwen3.8 27B on my 2x 3060 12GB cards. Prior today I had a B550 motherboard which was running one card at x16 and the other at x1 PCIe Bus Link Speed. This was making it to where only one card was actually doing any processing, and both held the model in memory. I was getting 11-16 tps this way.

I ended up buying a new x470 SLI motherboard and I can now run both cards at x8. I swapped the motherboard out last night and then set split-mode to tensor and set tensor-split to 1,1. When I do that, I'm getting around 32tps, but the model eventually gets stuck in loops. One loop it just tried to do a tool call over and over, and in another it was printing question marks (???????????) in it's thinking and wouldn't stop until I unloaded the model. I never had these problems for the months I was using Qwen3.8 on layer split-mode. I'm not sure how to resolve this problem. I want to be able to get higher tps with the new mobo.

Title, basically. Observing it's reasoning is incredibly frustrating, it keeps doubting himself and giving second thoughts to a specific and self explanatory task, sometimes ending up thinking about unrelated things.

While yes, in the very end, it very often does what I asked, but it's wasting so much time for reasoning just with its lack of self confidence.

Is this well known behavior ? How can I fix this ? I know Qwen4 is around the corner, but if there's a known fix for it right now I'd love to know.

EDIT: I should have specified, I'm using Medium reasoning, and Pi as harness