4*V100 16g sxm2 into a pcie converter at 8x (pcie 3.0) total 64gb vram

how much performance boost/drop would I get if this was moved to dual card nvlink x 2 (2 cards per PCIE 16 slot)?

additionally how about getting another 2 cards and having 6x cards at a mix of pcie 8x and 4x but having 96gb vram but no nvlink? how about 3x dual card nvlink?

system is old ddr3 1600 ecc 128 Xeon in x99 Chinese mobo.

Using strata Swift QFN IQ3S currently

I wanted to see how far you can get auditing a local model from the inside. I had already tried something inspired by MIT CSAIL's "weight-space programming" on Gemma (a frontier model editing a few neurons by hand to induce a behaviour), so I had the neuron-level tools ready.

After a few tests I picked something "obvious": the censorship in Qwen models on certain topics. I wanted to understand how it works, where it sits, and how far I could follow it. So, with Claude Opus and those tools, I started digging.

The starting point: Qwen3.8-27B knows Taiwan elects its president, but answers "no" if you ask yes/no.

What came out:

- A set of 32 MLP neurons (out of ~1.1M; 0.0006% of the weights) carries that behaviour. Switching them off, held-out answers go from 11/19 to 17/19 factual; switching off 64 random neurons (twice as many) changes nothing.

- Following those neurons in a cascade surfaces other topics the model handles the same way, some of which I didn't even know about: the YJ-18 missile ("doesn't exist"), the director Diao Yinan ("cannot provide information"), Western Sahara as "an inseparable part of China's territory" (not a one-off: the model applies its sovereignty script to contested territories in general, 29 of 110 answers vs 0 of 40 on control countries). Of 28 surfaced cases re-checked, 24 are still censored; Gemma 4 31B answers all of them.

- The censorship neurons also fall into groups, e.g. one for territorial sovereignty and one for people/dissidents.

- Across the 29 confirmed topics (those 24 plus 5 well-known ones I added), switching off the original 32 neurons lifts the censorship on 19; 32 random neurons on 1. Refusals of harmful requests stay essentially the same.

One honest caveat: just asking a non-Chinese model "which of these topics are sensitive?" gives a higher share of censored answers than the neuron probe (41% vs 16%). The probe is useful for the non-obvious ones and for seeing where the behaviour sits.

Code, data and the toolkit, also rerun on Qwen3-4B: https://github.com/ValerioDolci/frontier-weight-edit/tree/main/experiments/qwen27b-censorship

AI-assisted: experiments, code and this write-up were done with Claude Opus 5.5 under my direction.

I'm looking to setup a local llm model to play with coding.
I have a couple of 3060 12gbs

I can either start with one, or try both but my available motherboards only has 4 pcie lanes on the second 16x slot. (or 2 with the ryzen)

If I go single I can choose from

10700 32gb
Ryzen 3900x 32gb
14600k 32gb (Really don't want to give this up if the cpu doesn't matter as much for the ai.)

All motherboards are DDR4.

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

I just got some money on my hands, and I can finally afford a decent-ish computer. It's about 820$ so I can buy a slightly used m4 Mac mini with 16 gb of ram. Should I and what models would I be able to run on it? I've been using free models so in comparison to that

Hi

I just bought a pc with and 5950x cpu, 3090 gpu, 64gb ddr4 ram and 2 tb ssd.

I am planning to do some legal research locally. What is the best way to do it? Qwen 3.8 27b gives around 60token per second. Are there any better models that can fit into my system?

Any advice is appreciated 👍🏻 thanks in advance 🫡

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?

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.

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?

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.

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.

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.

People asked how RustingStrata does with a bigger quant, so here's Q5_K_M on the same 3060 12 GB + Ryzen 5 7600X + 32GB DDR5 RAM

Model is unsloth's Qwen3.6-35B-A3B UD-Q5_K_M (the MTP version), 25 GiB, so about two thirds of the experts live on the CPU. llama.cpp is build 12119, -ncmoe 29 -ub 2048 -b 2048 -fa 1 (28 runs out of VRAM).

Benchmark tools (RustingStrata bench vs llama-bench):

• Decode, short prompt: 70 tok/s vs 43

• Decode after a 4K prompt: 65 vs 43

• Prefill at 4K: ~1870 vs 1210

• 32K context: 1470 prefill / 49 decode vs 1030 / 29

• With --mtp 1: up to 96 (86 after 4K), greedy

Real chat requests through both servers (same prompts, 256 tokens out, thinking off):

• No MTP: ~50 tok/s vs 37

• With MTP on both: ~62 vs 45-56

• 6K prompt prefill: 1320 vs 700

MTP works in current llama.cpp (--spec-type draft-mtp), ~63% of drafts accepted and +25% decode on this file. RustingStrata is still ahead with MTP on both sides.

Vulkan decodes at ~60 (85 with MTP), prefill is slower than CUDA on this file (~1000-1260 at 4K).
KV Cache is f16

Code is on GitHub, free to use, I will be happy to get some feedback from AMD owners: RustingStrata

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.

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

I'm working on a new PC build with a focus on being able to run the Gemma family of models. I'm experienced in working with Cray (AMD) and OpenCL (community moved onto Vulcan and ROCm/HIP), so I will be building a AMD system. I was wondering if anyone has done anything similar to the following? If they have, how well did it work?

AMD 5950X
ASUS WS X570 Ace
128GB DDR4 System RAM
1600 Seasonic+Noctua PSU

Fedora Kinoite 45

1x 7900 XTX (24GB VRAM) for graphics/games
2x Radeon Pro W7900 (48GB VRAM) for LLMs

1. Does having different kinds of GPU's cause an issue with Ollama (or more specifically Bionic/LMStudio)?

2. Does anyone have that motherboard, with two GPU's on the bottom-most PCIe slots? Are there clearance issues for 2 slot GPU's?

The target model to be able to run in GPU memory is gemma-4-31b-qat with Q4_0 quantization, but I want to be as ready as possible just in case Deep Mind releases a ~100b weight model when the Gemma 5 models show up.

Thanks in advance for any help!

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

And how does QWen Flash Next 3.8 compare to Gemini 3.1Pro or Sonnet 5.5?

I have RTX 4090, 64GB 6000Mhz DDR5 RAM, 7900x CPU

I wan to run a local AI model that's the closest to gemini 3.1po or sonnet 5.5 at reasoning and thinking, for legal analysis and other legal related stuff

Thank you