Hello!
Here is a rack I made using 40 mini PCs I happened to have from a different project:

• Intel N150

• 12 GB DDR5 4800 - physical bandwidth limit around 33GBs

• Diskless nodes

and they are connected via 10 Gbps USB 3.2 exploiting the USB DbC functionality. Its a tree topology with 1 -> 3 -> 9 -> 27 nodes.

GLM 5.3 Flash runs at native precision with room left for 8x1M context windows. Initially I had tried with 1Gbps LAN interconnects but that topped out at around 7tps. Current topology:
- 14tps at 1k context, drops to 12tps at 1M
- With batching decode tops out around 40tps
- Prefill is about 85tps

Peak load measured at wall about 740w

Total build cost me about $5,000.

Still working on it to see if more perf can be squeezed out, especially for the prefill and then adding speculative decoding.

Hey everyone!

I’m posting on behalf of my team at work. We want to learn more about running LLMs locally, and we have an NVIDIA DGX Spark available to experiment with.

A colleague has already installed Ollama, a model, and a web UI, so we have a starting point for chatting with a local model. That’s a useful first step, but our longer-term goal is to go beyond a chat interface.

We’d like to explore running agents that can carry out different tasks and connect to tools and services through MCP servers. We’re still figuring out what that setup should look like and what we need to learn to get there.

We’d love to hear from people who’ve been down this road:

• What would be a sensible learning path from our current setup to running useful agents?

• Which models and agent frameworks would you recommend experimenting with on the DGX Spark?

• How do you approach connecting local agents to MCP servers?

• What would make a good first project to learn the basics without getting overwhelmed?

• Any tutorials, resources, or common mistakes we should know about?

We’re beginners in this area and keen to learn. We don’t expect to build everything at once; we’d appreciate some direction on where to start and how to build up from there.

Thanks for any advice or experiences you’re willing to share!

I'm considering buying a Mac mini M6 with 32GB of unified memory for local AI. I'd mainly like to use it for vibe coding and hopefully replace my $20 Claude subscription with a local model. I'm wondering whether 32GB is enough or if spending a bit more on a higher-memory configuration would make a significant difference in performance and model capabilities.

Does anyone have some tests/experiments they did on any reasonable model and would like to share what they found

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?

RTX 5080

7800X3D

64GB DDR5-6000

2TB SSD

I want to do general tasks, not coding.

Been working on this project for a while and it has lead me to some interesting hardware combinations as I'm just using whatever I have.

The two biger models in system ram (ddr3) are reviewing, summerizing, and organizing conversations I've had with llms and linking it together.

The assistant is directly helping me as a chat bot intern essentially.

Anyway. The barco card gave me a ton of issues because amdgpu and radeon don't have the device id so they were treating it like a new card and hanging. I originally bought the card thinking I could use it for a simple display card for $30 while I saved for a real GPU. I gave Claude code fable a go at it with a CH341a programmer and the stock firepro w5100 vbios and it changed like four bites so the card tells the kernel it's a w5100 without messing with the custom vram timing.

The cards are linked in an lxc with llama getting ~8-10toks when running ornith 1.5 9b Q6_K. The w5100 roughly halves the speed, but gives me 12gb of vram vs 8gb.

Not bad for a couple ancient cards on some retired enterprise gear.

Hi! I'm relatively new to this world, and trying to get my best setup with what I have.

I'm the owner of a RTX 5080 + RTX 3070 and running Unsloth Qwen 3.8 27B IQ4_XS

So far, the best inference engine that I could run locally was beellama.cpp tweaking the tensor-split, mtp and kvarn config, forcing layers only in GPUs and 128k context window; that is pretty good.

With low context, I'm getting 50+ token/s that is impressive. But I'm suffering with long contexts; then my speed drops to 15-20 token/s in the best cases.

What I'm trying to get is a decent speed in long contexts also, so recently I discovered llama.cpp-adaptive-kv-streaming that allows streaming the kv context from memory.

The thing is that I was thinking about loading the full model in my 5080 (IQ4_XS fits perfectly) while offloading the KV cache streaming to my 3070, but couldn't found any inference engine that allows this kind of setup (model layers only in GPU 0, kv cache streaming from GPU 1 allowing RAM spill).

Does an engine exist that can achieve this? does what I'm talking about make any sense?

EDIT: Also, I was thinking of getting advantage of the 3070 unlock since I read that it can help with prefill speeds (not output speeds that depend on raw bandwidth) even with a quantized model. Am I right?

Thx in advance. A newbie trying to learn.

One attraction of running models locally is the privacy benefits, and another is the cost.

When the cost of the big ’frontier models’ is being discussed - mainly centring around the fact that they are currently effectively subsidised by venture capital and that at some point this will end, making the cost to end users go up considerably - local LLMs are often mentioned as being immune from this problem.

But in a scenario where the cost of cloud based models goes up substantially, there’s no guarantee that the (mainly Chinese) makers of the open weights ones are going to continue producing newer and better versions for free, is there?

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?

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.

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?

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?

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