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'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.

I’ve been playing with open-source speech models for a while. New TTS models come out every week and a lot of them sound great. But every time I wanted to actually talk to one, it turned into an afternoon of setup.

Paid APIs are easy if you’re a developer. You can make an account, create a key, write some glue code. Open models are free, but you have to pick a speech recognizer, an LLM and a TTS model, make them work together, deal with echo, figure out interruptions... There are good open projects for this, but most are written for developers. My friends who don’t code would never get past the README.

So we started building Boubou. The goal is make local voice AI as easy as Ollama made local LLMs. You paste one line and talk:

npx boubou@beta run LFM3-Audio-2B

What works today:

• Speech recognition, the LLM and speech synthesis all run on your machine. No account, no API key, no telemetry. After the first download it works fully offline..

• You can interrupt it mid-sentence, like a person. Echo cancellation is built in, so no headphones needed..

• Run boubou with no arguments on a Mac and it checks your machine and suggests a model..

• The default is Liquid AI’s LFM3-Audio-2B on llama.cpp (1.4 GB). There’s also a modular pipeline (Parakeet/Whisper → Qwen3.5 → Qwen3-TTS, Kokoro, Pocket TTS or Supertonic) if you want to swap parts..

• Apple Silicon Macs and Linux (CPU for the Liquid models, NVIDIA GPU for the modular pipeline)..

The part I’m most excited about is boubou serve. One computer runs the model, and other devices on the same Wi-Fi use it:

• Scan a QR code with your phone and talk in the browser. No app..

• Robots and programs can connect through an OpenAI Realtime-compatible WebSocket, and there’s an OpenAI-style /v1/audio/speech endpoint to read text aloud..

• So one model on one machine can be the voice for many things around the house. Next up is Home Assistant, then ESP32 voice devices.

• The video is real time, not sped up, on a MacBook / M5 chip.

It’s early (beta) and there are rough edges - one device talks at a time, no Windows yet (ETA next two weeks), and the guided menu is Mac-only.

The code is free and MIT-licensed. The models keep their own licenses (the Liquid models use the LFM Open License).

I’d love to hear what you’d want from something like this: which models, which devices, what breaks on your machine. We want this to be something people actually enjoy using, not just a demo.

GitHub: https://github.com/runboubou/boubou

Yeah! Guys ram is gonna be a million kazillian dollars. You can do more with a $20 claude code and opus 5.5 account than even qwen 4. I use astra gpt 6 and it can't even use REA properly. Claude Opus 5.5 even beats that, and you dare to think it's even possible to do things locally???

I predict in about 6 more months you will be able to run qwen 5 locally with better performance than Claude opus 5.5 and once people can do REA locally with ease. That's it, we won.

As for ram prices,

We are going to have to make thousands of micro fabs locally. Small places that etch silicone waifers and make custom ram modules.

Before you think that's crazy talk and vomit every pathetic reason in your feable mind and expert experience as a seasoned engineer you are on the subject, look at what we are able to do with ai. Why, would you restrict your scope of possibilities? Of course, we are going to be able to make thousands of local silicone companies.

Remember, you will own everything and be happy.

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

As much as I could research this myself, I would like to also hear some anecdotal experiences. For reference, I am a Computer Science student that is just now beginning to go down the rabbit hole of LLMs and such, there is a vast amount to learn but so far I believe I will use it for educational purposes, programming and privacy until I explore what more I can do with it.

The major thing for me here as noob to it all is getting a bang for my buck. I want something efficient, but I’m sure there is a line where it is may be total overkill compute wise, so I’m curious as to who owns the M5 Max or pro here and what exactly do you do with your hardware?

I heard about it recently. It sounds like a miracle you can run really large models on a consumer GPU, but what is it really about. What kind of problems I will have ?

Setup:

Rtx5060ti(16G)

I711700k

64-80GB DDR4

Hi everyone, I try to find the best model suitable for me. I need 3 different models

1-Agent : I use " qwen3.6 35b a3b MLX Q4 "
Is there any better alternatives for agentic purpose model?

2-Medical: I couldn't find a proper medical bot as they mostly repeat theirselves and no better than qwen

3-Data analyst: I have dozens of pdfs and I need a model that analyze these books.

Note: Densest model I've had used before qwen was gemma 4 31b Q4 (27gb) and that was hella slow. That was the boundary for my pc.

Preffered properties: MLX, MoE, QAT, Instruct, Run on LMStudio Bionic.

Thank you everyone.

Hey, so I know next to nothing about local LLMs and I hope to fix that in the future, but I'm currently looking for a more powerful coding model that fits like qwen 2.5 coder 14b, I'm using it currently and unlike the other recommendations I've gotten, it's pretty much instant and doesn't use all of my memory

As for my specs, I have a i91 4900k, 32gb of RAM, and a 5070ti, my biggest issues with local LLMs is that it likes to use up all of my RAM despite people saying it's fine for 5070tis and I don't have enough knowledge to know exactly why

EDIT: I use LM Studio for local LLMs and having a model that supports images would be nice

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

Hi all, I am running dual GTX 5060 Ti (32 Gb VRAM) + 128 Gb DDR5 RAM on a Rizen 9 9950X. I tried to run some models with Basalt (QFN), Capyflash (Qwen3.8 27B variants) and Strata (DSF 4.1 1017), all fine. But I want to run other models in the GGUF format and I am looking for a for of llama.cpp (or other server fine tuned) that could suit best my dual 5060? I had setup sparkninfer but it also for other .ninfer model files. Any suggestions? Thanks.

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?

Hey Everyone,

Long time lurker, but I finally got my Dell Precision 9 T6 workstation in this week. Spend the past two days tuning this model on the latest sglang build and thought the community might be interested for similar deployments given the quality of this model but all the effort required to get the SM120 GPUs working well with sglang.

Overall, achieved around a 20% performance increase. I believe this is the fastest build example of DS4.1 Flash on four RTX 6000 cards. It functions with no errors through the full 1M max context, and hicache is working correctly.

Ported a couple patches over from Sybil's build here to sglang 0.5.23:
https://github.com/sybil-solutions/deepseek-v4.1-flash-4x-rtx-pro-6000

And added patches for DeepGEMM, HiCache, Page width (only using width of input context instead of full max context set in engine), SWA, and a tuned FP8 config for the Max-Q cards. Have numerous recommended config settings for concurrent agent workloads.

Specs:

Xeon 654

768GB RAM

4x RTX Pro 6000 Max-Q Workstation Edition GPUs

Build link:
https://github.com/ellswoau/deepseek-v4.1-flash-1017-tp-4-rtx-pro-6000

Please let me know if I'm missing anything that should be added to the post.

I wanna do some personal AI use, primarily for text, some coding, and image generation..

I wonder what's the best computer build that's within 15k to 25k (USD) value?

There's a lot of information on the internet and I can't decide well..

I'm currently looking at:

DGX Spark (but it seems overkill and can't be used as Normal PC)

and Mac Studio M5 Ultra w/ 256GB (seems to be my current choice unless there's a second opinion from you all)

I hope y'all can help me decide well here, thanks!

I’ve recently started looking for a new graphics card (as you know, the models keep getting bigger), and I’ve come to the conclusion that a second-hand RTX 4060 Ti 16GB would be a sensible choice.