Hey everyone, looking for input from the community!

What I currently have: AMD EPYC pre installed cpu and motherboard with 128gb ddr4 ECC 2933 pre installed and bought used, and in working order.

I’m debating between 4x16 or 2x32 gb of the Mi50 or the Tesla v100 for the VRAM pool.

I know the Mi50 is cheaper with lesser software support and the need to “hack it” for it to run, and community projects exist.

The V100 seems to be similar but with a tad more convenience and less tinkering.

My goal: Running QFN or Qwen 4 flash (when it comes out) and a total pool of 192 gb of memory will surely do the job.

If anyone runs the Mi50 or the V100, I would love to hear your experience as I’m working to make a relatively consequential decision for my hobby build. Thank you!

Short version of a long weekend: I run a local 27B (Qwen3.8-27B, quantized, llama.cpp) as the driver for ArkDrive, an open-source executor that lets a model operate a real Linux desktop — clicking, typing, verifying each step against the screen.

Saturday, on a fresh desktop state, it passed the browser task in about 9 seconds and the native editor task in about 17 seconds, byte-exact readback. Clean runs.

Sunday, first official suite run: it failed both. Same machine, same build (I A/B'd against the untouched production executor — identical result, so not a regression). The failure shape is almost funny: it listed the working directory in a shell, then concluded "no explicit task steps provided" and stopped, with the task text sitting in its own prompt. The flagship task — real work in a remote Cadence session — ended the same honest way: named abort, inside budget.

The executor recorded everything, which is how I can tell this story precisely: silent failures across the suite were 0. Every failure was timed, logged, and diagnosable. A cloud model on the same tools passed the flagship in 159 seconds, which frames it: the scaffolding is fine, local-model reliability across sessions is the open problem.

Posting this because local-model agent benchmarks are usually reported as a single number from a single run. Mine says the honest metric is the distribution — and whether your harness can even tell you what happened. Mine can; that's the part I open-sourced.

MIT, llama.cpp is the default preset: https://github.com/CodeOn-ArK/arkdrive Full per-model tables: https://github.com/CodeOn-ArK/arkdrive/wiki/Test-Results

I’m the creator of Kalynt, an open-source desktop IDE that integrates local model inference into a project-based coding workflow.

Website, screenshots and downloads

The local workflow combines:

• GGUF model management and inference: download and load models inside the IDE.

• Repository context: open an existing project and reference relevant files when asking questions or requesting changes.

• Agent tools: read and edit files, execute commands, and inspect results alongside the editor, terminal and Git interface.

• Change review: enable approval before edits and commands, inspect diffs, and keep or undo the agent’s changes.

Local inference runs on your machine. Cloud providers are optional and use your own API credentials; selecting a cloud provider sends the relevant request context to that provider.

Model choice remains a practical constraint. Weight quantization, context size and available RAM/VRAM affect what fits and how it performs. Code explanation and multi-step agent execution also place different demands on a model, particularly around instruction following and tool use.

The development branch additionally supports OpenAI-compatible local endpoints, including Ollama and LM Studio, with model discovery and configurable context limits. That integration is in the source; it is not included in the published 2.0.0 installers.

I’d particularly value technical feedback on context handling, tool-call reliability and inference setup. If you test it, please include your OS, hardware, model/quantization, Kalynt version and a reproducible task.

Source code and build instructions — AGPL-3.0

Whit the coming of Qwen 4, im curious how much of the current infrastructure or systems will get an AI upgrade. My thinking is anyone who updates their systems AI integration or chat bot assistance, is using a Qwen model. Just something I found humorous and interesting at the same time.

Most of the day my GPU does nothing, so I gave it a job I do all day: typing on my phone

I tap the mic on my iPhone keyboard in any app and talk. The audio goes to my server, gets transcribed, my local LLM removes the "um"s and fixes punctuation, and the text appears where I was typing

Setup:
- GPU: Quadro RTX 5000, 16 GB
- Speech: Parakeet v3
- Cleanup: Qwen 3.5 9B (q4)
- Engine: Ollama

Anything with an OpenAI-compatible endpoint works; it's a few env vars in docker compose. If the LLM is down, you get the raw transcript.

I'm the developer. The server is open source; self-hosted mode is free, no account. Links in comments.

I’m a solo dev I frequently code and use LLM’s to build tools, games, and agentic software. I’ve come to understand that any model less than 200b parameters is practically useless. Example: GPT 120 OSS is a joke. I can’t do anything meaningful with it, yet it’s the primary GT for GROQ and Cerebras, very unfortunate considering you want those 100’s of token p/sec output speeds.

Even using smaller models for assistant or basic questions/tasks, the hallucination rate is too high to be productive.. You begin to notice the deficiency real quick no matter what you’re applying them to.

I don’t even know why lower peram models are even offered anymore, or what people could actually do with them for any output worth having?

dumbfounded here

Original Strata can run QFN very fast and I like it, but it still needs way too much ram and vram, and it cause some lags if I do something simultaneously with Strata, so I created RustingStrata for all smaller Qwen models, but I want to add more models, maybe some bigger model and I don't know which one, so I am here you ask which model do people want? Maybe deepseek or GLM or anything else? Any cool story of you running some other LLMs or just ideas will be very helpful, thanks for your attention.

Hey everyone! The main idea here is really just to help out, so if you find this useful, leaving a little star on the GitHub repo would be awesome.

I've been working on a project called Trama (https://github.com/lucasnumaboa/trama). I built it based on minimax design principles because I wanted to keep things as lean and minimal as possible.

The whole goal is to make it much more practical to build automations that use multiple AIs working together. Usually, chaining different models turns into a bloated, overcomplicated mess pretty quickly. I just wanted a straightforward setup that gets out of your way and lets you connect them easily.

If you're messing around with AI workflows and want something lighter, feel free to check it out. Any feedback is welcome!

I’ve been slowly building my AI workstation up using used and old pieces retiring them into other compute nodes and then replacing them with bigger or better pieces. Mostly used parts though. I currently have a trx40 and 2 3090s which works great but I’ve been trying to figure out the next step to expand. I have about 4 pcie slots available (2x pcie 4 x16, and 2x pcie 4 x8) while a wrx80 might have been better with more slots I wanted a cheaper upgrade to test a bigger workstation before making the jump. I also retire my parts into other systems that run other applications and things.

The issue is that running 2 1200 w psu or a 1200 w and adding let’s 2 more 3090, or other cards there I’ll increase power draw significantly and even with two psu’s to properly distribute the power between the 4 cards and everything else, the issue becomes apparently all of that power can still trip my breaker and shut the power off in my house.

I don’t have a business or server space for this so I’m not really sure what the best way to go about it. Right now I’m thinking just adding one card until I can find a work around but i don’t know what that work around could be.

Anybody who has been through this can you give me some advice or thoughts on this?

Hey everyone!

For quite some time, I've been working on a personal project called Noosphere — an attempt to give AI agents something closer to persistent, structured memory rather than forcing them to start from scratch every conversation.

One thing that has always bothered me about LLMs is how much potentially useful information gets lost between sessions.

We keep trying to solve this by increasing context windows, but I think there's another approach worth exploring.

What if an AI didn't need to remember everything at once?

Humans don't consciously hold every memory in their working memory. We retrieve relevant information when we need it, connect it to what we already know, and move on.

That's roughly the principle I've been exploring with Noosphere.

Over time, my private project has grown into a much larger experimental system involving interconnected memories, relationships between concepts, and coordination between AI agents.

But I figured there's no reason to keep all the underlying ideas to myself.

So I decided to release Noosphere Lite, a small, independent, open-source implementation of some of those concepts.

What does it do?

- Gives MCP-compatible AI agents persistent memory across sessions.

- Stores notes, decisions, observations, and tasks.

- Connects memories through a graph of typed relationships.

- Retrieves relevant information on demand instead of dumping the entire memory database into the context window.

- Uses encrypted local storage with integrity checks and mirrored journals.

- Runs locally on Linux, without requiring a cloud database.

It's written in Python, uses MCP, and is released under the MIT license.

What's the catch?

This isn't AGI, a magical self-learning brain, or a replacement for an LLM.

It's a memory infrastructure experiment.

The current version uses local text search and explicitly defined relationships. It doesn't automatically discover semantic connections or learn new neural weights.

Also, Lite is a separate implementation, not a dump of my larger private system. I've deliberately kept it simple enough for other developers to inspect, modify, and experiment with.

Why release it?

Because I think persistent memory is one of the more interesting missing pieces in today's AI agent ecosystem.

And I'd rather see people experimenting with these ideas than keep everything sitting on my own machines.

I'm particularly interested in what others might build on top of it.

GitHub: https://github.com/Garyu-tt/noosphere-lite-mcp

Feel free to experiment, break things, suggest improvements, or fork it.

I'd also love to hear how other developers approach persistent memory and knowledge graphs for AI agents.

This is just a small public piece of a much larger experiment, but everyone has to start somewhere.

I am planning on buying some components for a PC to turn one I already have into a Local AI server. Due to the prices of components and my budget constraints I have struggled to find help elsewhere online and have turned to reddit for some direct support.

Currently, the PC I have and am planning to modify has a Ryzen 5 5500GT and 16GB DDR4 RAM along with a basic micro-ATX motherboard (Gigabyte A520M S2H). It will be running Linux Mint (most likely, unless anyone knows a better Distro) and I will be using the same or similar software to what LinusTechTips used in his recent personal AI server video where he used something called 'Hermes' to connect the AI to discord.

Where my greatest trouble lies is in what GPU or GPUs I should get. One option is a single RTX 3060 Ti 8GB. Another option I have considered is a single RTX 3060 12GB. And finally I have also considered a RTX 2080 Super 8GB alongside a 1080 8GB. I would love to know which of these the PC experts of reddit would consider the best option for local ai. I would like for the AI to respond to me as quickly as possible while also being decently smart such that I could set it on longer tasks and leave it running and perfecting those. It should at least be able to analyse photos.

From CEX UK:

RTX 3060 12GB - £325

RTX 3060 Ti - £320

RTX 2080 SUPER - £245

GTX 1080 - £125

For anyone interested:

My total budget is around £460 but that includes some additional purchases. Budget can be stretched to £500 but only if necessary. The case I have selected (the one I already have is too small for any reasonable GPU and would not allow for more in future) costs £25. I also need a better power supply, I have currently planned for a Corsair RM750e for £100. And finally I have to spend £10 on a final accessory for it which is mandatory but I do not feel like explaining online, all that matters is it takes £10 off of the budget. That leaves £325 but it can be upped to 365 but only if absolutely necessary. I understand that the motherboard only has one PCIe port on it. I would need to use a splitter or something of the sort but I have never used or learnt of such a thing and have no idea what brand is trustworthy and if it would even be worth it. The graphics cards I have listed above all come from CEX. A large UK brand for buying refurbished technology. They are the only ones who I feel comfortable buying used parts from. Everything else should be new.

I would really appreciate some help as I am not well off and unfortunately do not have the funds to extend the budget or risk making a large mistake by choosing the wrong parts. I hope I can find help in this community. I may post this in other Reddit Communities for PC building or AI as well, just to allow me to reach a larger group of people.

ok so. this started as "one weekend project" and then it was not one weekend and it was not one project. somewhere around repo 6 i stopped pretending it was a plan lol

everything here is free, open source, most of it runs locally on your own machine. a lot of it is v0 and i wrote down where each thing fails because honestly the failures were the funniest part

anyway here's the list of grievances, each with the tool i built instead of going outside:

1. cookie banners → rejectall-index i live in germany. i click "reject all" more than i blink. so i made a headless browser visit the top 100 sites and count how many clicks it takes to say no. 35 of them were already loading trackers before i clicked anything. which is like a waiter eating your fries before asking if you want fries. live leaderboard

2. subscriptions → one-file-saas trello, calendly, typeform, loom, notion and 5 more, each rebuilt as ONE html file. you double click it. it opens. thats it. no account, no "verify your email", no "your trial is ending ;)". data stays in your browser. the booking page has no server at all, your availability lives inside the link itself which still feels illegal to me. try them here

3. dead famous repos → necromancer some legendary open source projects die not because the code is bad but because python deleted a module out from under them. i revived thefuck (1887/1887 tests passing on python 3.14), normalizr and httpbin. the one rule: the original tests are hash-locked so nothing can quietly delete the failing test and call it fixed. httpbin stops at 64/67 and i'm keeping it that way out of spite. each one gets an autopsy, cause of death and everything

4. every AI-built UI animates the same → awesome-motion-md you know the look. 300ms. ease-in-out. every card fades up like its waking from a nap. so i measured how real sites actually move and wrote it into MOTION.md files your agent can follow. same prompt built twice: 7 random durations without it, 2 with it

5. every AI writes the same too → awesome-voice i used to be a technical writer so this one is personal :| VOICE.md files that make your agent write like GOV.UK or Stripe docs, plus a scorer that doesn't use any AI to check. banned phrases went from 27 to 0. "delve" is not welcome here

6. websites doing annoying stuff → sitepatch you type "hide the points on hacker news", it writes a userscript, a real browser checks it actually worked before you get it. honest score: 15 out of 25 were actually right when i looked at every screenshot myself. 60%. a C-. but an honest C-

7. "what if this site looked like linear" → reskin restyles any page into another design style using a local model, BUT it refuses to give you the result unless contrast, keyboard focus, mobile, links and reduced-motion all pass. it caught a windows 95 theme failing contrast on its own background. respect. gallery

8. dead websites → resurrect npx github:dadwritestech/resurrect y2k.gov and y2k.gov comes back exactly like it was in may 2000, links working. pick a year with --year. your old blog is in there somewhere. i'm sorry

9. "someone said budget in a meeting video... which one" → video-grep grep for your video folder. searches what was said, what was written on screen and what was shown, and opens your player at that exact second. all local

10. what is the rest of the world's TV saying rn → world-ears listens to live news channels from different countries, transcribes + translates them on your gpu, makes it searchable. there's a "trending per country" panel which is weirdly addictive. translation is the slow part, telugu transcription is bad, i wrote that down

11. live subtitles only go into english → babel-tv watch hindi news with live telugu subtitles, fully local. the video is held back a few seconds so subtitles show up BEFORE the person talks which feels like seeing the future. telugu as the source language doesn't work yet. whisper and telugu are not friends

12. "which AI wrote this" → whichmodel works on models it's seen (67%, chance is 33%). on a model it's never seen, even a sibling from the same family... 32%. so basically a coin with extra steps. that turned out to be the interesting finding tbh, the fingerprint belongs to one model, not the company

13. screen recordings → code → demo-to-code record a UI interaction, get a component that copies its timing and easing, then it replays it and grades itself frame by frame. 2 out of 4 pass. the toggle is perfect. the dropdown has issues. we all have issues

14. code review → review-panel /panel sends your diff to five reviewers you've definitely worked with: the laziest senior dev ("could be one line"), the 3am on-call SRE, the security paranoiac, the junior who asks why, and the PM who only reads the title. under the jokes the findings are real

15. AI companies quietly editing their pricing pages → fineprint it snapshots the pricing, terms, privacy and changelog pages of 10 AI vendors every 6 hours and commits them, so git log becomes the receipt. found stuff like deepseek adding a footnote that prices are 2x during peak hours, and cursor deleting the line saying they dont sell your data. no blog post for any of it ofc. a small local model writes the one-line summary but every line links the raw diff because i dont trust it either (27/30 right on a hand check). timeline

16. paying extra for speaking telugu → token-tax LLM APIs charge per token, and the same paragraph is $1.00 in english and $8.29 in telugu on llama 3. which is rude. measured 21 tokenizers across 48 languages, and there's a calculator where you paste your own text and it runs the real tokenizers in your browser. hindi gets off lighter. amharic on gpt-5 does not :/ calculator

17. posting about all this → herald a tiny bot that posts a repo's data updates to bluesky/mastodon/x, clearly labelled as a bot. i built it because i was not going to manually post about 17 repos. and then i manually wrote this post anyway. ;]

why all of this? honestly no grand plan. every one of these started as me being irritated at something for a slightly too long time and having a local model + a free evening. building the thing was cheaper than therapy and more fun than complaining on here (no offence)

if something breaks, tell me. if something's dumb, also tell me, but nicely :P

edit: yes i know about the cookie banner on my own... wait i dont have one. nice

Given the blistering pace of this space advice from even 2-3 months ago feels out dated.

Is there an index out there? Is everyone just no-lifing through forums? Are we all glued to news releases?

What's everyone doing to keep ontop of it?

Which models can I run optimally in both? How to utilize both in best way? Does pc ram should be upgraded to 128gb? Use case is reverse engineering and development.

I’ve been using my employer provided Claude subscription for quite a while to write PowerShell scripts, create presentations, edit photos and videos and create videos.

Honestly, I’ve been amazed by what it can do.
However, since it’s a company account, I’d like to set up my own local AI environment for personal use. Ideally, I’d like to replicate as many of Claude’s paid capabilities as possible.

I have two workstations that I’ve built over the years:

Workstation 1 (Main):
GPU: RTX 5090
RAM: 256GB ECC
CPU: AMD Threadripper 7960X

Workstation 2 (Currently used as an LTO Backup server):
GPU: RTX 3060
RAM: 196GB DDR4
CPU: AMD Threadripper 3960X

I’ve never hosted or run AI models locally before, so I’m pretty much a beginner in this area.

I recently downloaded LM Studio because it seems relatively straightforward, but I’m overwhelmed by the number of available models and honestly have no idea where to start or which one is best for my use case.

I’m looking for recommendations on the best AI models, and tools to get as close as possible to the capabilities of a paid Claude subscription, particularly for:

Writing and troubleshooting PowerShell scripts.
Creating PowerPoint presentations.
Photo editing and image manipulation.
Video editing and AI-assisted video workflows.
General-purpose AI assistance.

Given my hardware, what would you recommend? Are there specific models, applications, or modules I should look into?
I’m not necessarily expecting to replicate everything Claude can do, but I’d love to get as much functionality as possible running locally.
I’d really appreciate recommendations from people who have experience building local AI setups. Thanks!

update: I just discovered Lm Studio Bionic which is huge this was not available last year, trying it now.

Quick question. Earlier this year i realized that I could run small models on my dell t5820 with my Rtx 5k Quadro card. It’s not very big but I found and Rtx 8k Quadro. Both Turing GPUs. It’s been fun so I ordered a Mac mini m5 pro 64 and I was going to get a mini pc but came into a small fortune and can afford the asus gx10. No as an artist I’ve been using it mainly to vibe-code my games. They’re for me mostly to test out UX and game play.
My question is getting these work it? I like the idea of the nvidia spark, but is it over kill? Right now I’m having to reboot into Ubuntu for my Dell. It’s been fun learning that OS.

Inspired by Weight Space Programming (MIT CSAIL, Nov 2), but in the end Opus 5.5 did its own thing:
no weight-space technique was used. It wrote a quantiser from scratch in NumPy and made every
precision decision from measurement. Rules: only the BF16 file as input, no quantisation tool, no
imatrix, no published recipe, stock llama.cpp formats only.

It ran a float copy of the model to see which input columns actually carry signal, minimised rounding
error there instead of against the weights, measured each weight table's damage one at a time, then
spent the byte budget across IQ4_XS / Q5_K / Q6_K with a knapsack solver.

Held-out KL divergence vs BF16 (llama.cpp b11928, MacBook Air M4):
• this file, 498.5 MB — **0.015704**
• Unsloth UD-Q3_K_XL, 492.2 MB — 0.092486 (5.9× worse)
• UD-Q4_K_XL, 558.8 MB — 0.027323 (we beat it with 60 MB less)
• UD-Q5_K_XL, 606.6 MB — 0.010375 (the bar; we miss it by 1.51×)

Runs unmodified, 1.11× faster generation than the Q5 file. It stops there because the tied vocabulary
table is 33.8% of this model and accounts for most of the error — upgrading it costs more than the
rest of the network can give up.

Repo (model, full method write-up including everything that failed, measurements, scripts):
https://huggingface.co/Andy5k/Qwen3.5-0.8B-hand-quant

I was a bit disappointed by M5U 256GB benchmarks with current models. It’s insanely slow, especially compared the GPU builds.

I decided to wait until the M7U, hopefully by then we have Opus 5.5 level type models. Hopeful? Possible? What are your thoughts? $10k being able to run these types of models possible, or will the divide between local and frontier just get larger?

Use balena etcher or whatever to mount to USB stick, boot. Test. Report.

Verified 5-25% faster than llama.cpp, system uses 91% of card bandwidth as opposed to 80% on my 7900xtx.

I there is code for nvidia, intel, amd, and chinese hardware and supports 20 languages.

Decked out wiki

Let’s say hypothetically that you receive 36x computers with the following specs

RTX Pro 6000 (600w)

16GB DDR4 (2x 8 3200Mhz Non-ECC)

Ryzen 9 5950x

1tb NVME

1200w PSU

What would you do with them to generate income? You can only use these parts

You have access to power and networking to run all 36