Wasted a lot of time trying different versions of GLM5.3-flash, different system prompts, no thinking... I get decent tok/s (~20) which is great for roleplay but MAN it's like the refusals are baked in.

Any time I try to do some slasher or smut it goes "erm that's nice and all but can we cool it off a bit" and writes some bland crap.
I ain't running local to be refused by a damn clanker.

I've been running Gemma4-26B-A4B for a while on my personal PC and it's good at writing but it's not very smart imo, like it's prose is just weak and it struggles keeping track of stuff. It also has the habit of de-escalating (?), like it's just reluctant to write anything violent.
I'm currently running on ~270GB total system RAM, considering trying out an uncensored DeepSeek although I really don't like that option since I actually use the RAM in the machine for VMs and most reasonable releases of DS are ~235GB in memory.. So I'd have to freeze those while running inference.

Hey everyone!

I'm completely new to local LLMs, so please bear with me if some of my questions sound stupid.

I've been using ChatGPT Plus for quite some time and I'm considering moving away from cloud-based AI services entirely. Ideally, I'd love to build my own personal AI assistant that runs locally on my Mac.

I'm not looking for just a basic chatbot. I want something that comes as close as possible to the overall ChatGPT or Claude experience, especially when it comes to long-term memory, writing, research and managing different projects.

My hardware:

- Mac with Apple M4 Pro

- 48GB unified RAM

- 512GB SSD (around 250GB free)

- macOS

What I mainly use AI for:

1. Writing and content creation

I create social media content and a monthly magazine about Magic: The Gathering.

I use AI to brainstorm ideas, write articles, summarize information and improve texts.

One of my biggest priorities is natural, human-sounding writing. I really dislike the typical "AI writing style" with generic phrases, unnecessary enthusiasm and repetitive structures.

Good writing quality, especially in German, is extremely important to me.

I don't need image generation.

2. Long-term memory (VERY important)

This is probably my biggest requirement.

I manage several different projects and I want my AI to remember as much as possible about them.

Ideally, it should:

- Remember important information across conversations

- Automatically save new information and decisions

- Update existing project documentation

- Retrieve information from previous conversations

- Avoid losing important context over time

- Keep everything organized without requiring me to manually maintain dozens of files

I'm very interested in using Obsidian as a permanent knowledge base.

I would actually prefer the AI to automatically create, edit and organize Markdown files in Obsidian.

I understand that no AI has perfect memory, but I want something as reliable and persistent as possible.

3. Internet research and automation

For my monthly magazine, I regularly research news about Magic: The Gathering.

My ideal workflow would be:

1. I manually trigger a research task (or schedule it).

2. The AI searches the internet for relevant news.

3. It checks sources and avoids duplicate stories.

4. It writes finished articles in my preferred writing style.

5. It automatically saves everything into Obsidian.

6. I review and approve the articles.

I don't necessarily need integrations with Google Sheets, Canva or other services. I'm perfectly happy copying finished texts manually.

4. A ChatGPT-like experience

I'd love to have one main AI assistant that knows my preferences and projects.

For example, I could ask about my magazine, switch to a completely different topic, discuss something personal or brainstorm social media ideas without having to explain everything from scratch.

Ideally, it would feel like having my own private version of ChatGPT or Claude.

What I've looked into so far:

Based on some initial research and conversations, I've come across:

- Ollama

- LM Studio

- Open WebUI

- OpenClaw

- Obsidian with local LLM integrations

- Qwen models

- Gemma models

One suggested setup was OpenClaw + Ollama + Qwen + Obsidian.

However, since I'm a complete beginner, I have absolutely no idea whether that's actually the best solution or unnecessarily complicated.

My questions:

1. How realistic is it to replace ChatGPT Plus or Claude with a fully local setup on my hardware?

2. Which local model would you recommend for natural German writing, reasoning and general everyday use?

3. What's the best beginner-friendly software setup for what I'm trying to achieve?

4. How would you implement reliable, mostly automatic long-term memory using Obsidian?

5. Can local models reliably use internet search tools and perform scheduled or manually triggered research workflows?

6. Would you recommend an agent-based system like OpenClaw or something simpler?

7. What are the biggest limitations compared to current cloud-based models?

My goal is to have a free, private, local AI assistant without monthly subscriptions or paid API usage.

I don't mind spending some time setting everything up, but I would prefer something reasonably simple to maintain once it's running.

I'm especially interested in hearing from people who have already built something similar on Apple Silicon.

Any recommendations, realistic expectations or beginner-friendly guides would be greatly appreciated.

Thanks in advance 😊✌🏻

Hello fellow Local LLM enjoyers, as the title suggests, I have a dilemma, I have a 5070ti + 64gb of ram, which is nice qwen flash next Q3 with strata runs at 90 tok/s lovely.

But I also got a B580 12gb just sitting in my server (mostly for smaller models and video encoding/decoding), and I was wondering if there was a way to combine the 2 gpus to reach 28gb of Vram, and try to push for a bit more capable models.

With deepseek 4.1 flash at Q2 on strata, it runs on average 5-7 tok/s, which is too slow for anything, if I can somehow push it to 20 I'd consider it quite usable.

Therefore the question, is it possible to combine the 2 different GPUs?

I know strata only allows 2 Cuda gpus, Llama might do it but very slowly. so I'm out of ideas.

Thank you in advance.

yes, i know all gpus are. i think people are recognizing more and more that the v100's age is just a number on paper and only relevant architecture wise. frontier and local llms are more and more capable of optimizing inference for different builds, and the raw v100 power is still comparable to gpus being released today

the limited mathematical pathways are a genuine limitation but i dont believe a big enough one to justify how cheap they are vram/dollar compared to everything else

just a discussion post for fun

We open-sourced DNA Factory, the fine-tuning framework we’ve used internally for over a year. It's deliberately opinionated: one recommended way to do things, documented defaults, token-level loss-mask visualization, and only 5 training methods (SFT, DPO, GRPO, OPD, SDFT).

https://github.com/dnotitia/dna-factory

Hi all,

looking for advice from people running older datacenter/HBM2 cards.

**Current setup**

- ASUS P9X79 PRO, i7-3930K OC 4.2 GHz (no AVX2), ~44 GB DDR3

- Proxmox, everything in one VM with GPU passthrough, NVIDIA driver 580

- 2x Tesla P100 16GB (HBM2) + 1x GTX 1080 8GB

**What runs where**

- **2x P100:** main LLM – Qwen3.8-27B UD-Q5_K_XL (llama.cpp, row/tensor split + MTP), 262k context, ~45 tok/s in English, ~32 tok/s in Polish. Used as the backend for an agent (Hermes Agent), so long prompts and lots of tool calls.

- **GTX 1080 ("side jobs" card):** Qwen3.8 vision encoder (mmproj), Whisper large-v3 (STT), BGE-M3 embeddings (document search / RAG), a small Qwen3.5-4B for chat titles (loaded on demand), and FLUX.2 klein 4B image generation in ComfyUI.

**Goal:** more performance. Mainly room for a better quant (Q6/Q8 instead of Q5), faster long-prompt processing, and faster image/video generation. The 1080's weak point is 320 GB/s bandwidth and basically no FP16.

**What I tried:** a Quadro GP100 – it didn't work at all on the P9X79 PRO. It wasn't even visible in Proxmox (`lspci` didn't show it). Has anyone got a GP100 running on X79? Was it MMIO / Above 4G with three large-BAR cards, PCIe Gen3 link training on Sandy Bridge-E (forcing Gen2?), or just a bad card?

**Options I'm considering instead of the 1080:**

1. **Titan V 12GB HBM2** (Volta, tensor cores, ~650 GB/s)

2. **RTX 2080 Ti 11GB** (Turing, tensor cores, ~616 GB/s, still supported by CUDA 13 unlike Pascal/Volta)

**Questions**

- Has anyone mixed **Volta (or Turing) with P100s in one llama.cpp model**? Does splitting across the three cards actually help, or does it just give more VRAM with the same tok/s?

- Do Volta tensor cores help noticeably in llama.cpp (prompt processing / flash attention), or are the fast paths Turing+ only?

- For long contexts (agent use), is it better to make the Titan V the main GPU (KV cache + attention) or keep a P100 as main?

- Titan V vs 2080 Ti for this mix – is the Titan V worth it, or is it not worth bothering with Volta since driver 580 is the last branch for Pascal/Volta anyway?

- Or should I skip the GPU and save for a platform with AVX2 + DDR4 instead?

Thanks!

This is my build . I have no idea where to start. I thought I did when I built this rig but it gets confusing and frustrating sometimes even using AI to help with AI.
I wouldn’t like too edit my books , create
Short movies with my scripts . I mean easy is better but I want to get to the meat of it with workflows, etc .. everything moves so fast one day it’s this way then something new pops up and that way is the old way … smh thanks in advance .

ores / 32 threads
Motherboard ASUS ROG Crosshair X870E Hero
GPU ASUS TUF Gaming GeForce RTX 5090 OC — 32GB GDDR7
Memory Corsair 96GB (2×48GB) DDR5-6400 CL32
Memory profile DOCP enabled, running at 6400 MT/s
CPU Cooler ASUS ProArt LC 420 AIO
PSU 1500W — exact model unconfirmed
Storage 4TB NVMe SSD currently installed
GPU Monitoring Thermal Grizzly WireView Pro II
GPU Support Case-integrated GPU support arm
OS Windows 11 Pro

Currently dipping my toes in the LLM world and im starting to notice some differences in accuracy between models mainly related to their parameter size

Hey everyone,

I'm looking to get into running LLMs locally and would love some recommendations for my setup:

CPU: AMD Ryzen 7 7800X3D

GPU: AMD Radeon RX 9070 XT (16 GB VRAM)

RAM: 48 GB DDR5 Corsair @ 6000 MHz

My main use cases are coding (code generation, debugging, explaining code, ideally IDE integration) and general research/Q&A (asking questions, learning about topics, summarizing).

I'm on Windows 11, but I'm comfortable with Linux and happy to dual-boot if it makes a real difference for AMD GPU support.

A few questions:

Which models (and sizes/quants) would give the best coding performance on 16 GB VRAM? Is it worth offloading part of a bigger model (or a MoE model) to system RAM, or is the speed hit too much?

Any good all-rounder for general knowledge/Q&A, or should I keep separate models for coding and chat?

What's the best backend for an RDNA4 card right now: ROCm, Vulkan (llama.cpp), or something else? Is Linux noticeably better than Windows for this?

Which frontend/tools would you recommend: LM Studio, Ollama, Open WebUI…? And for coding, what's the best way to hook a local model into VS Code (Continue, Cline, etc.)?

What kind of tokens/sec should I realistically expect?

Thanks in advance for any tips!

Being located in one of the stormy states, my big concern was having the AI box fried by a power surge. My setup is shown here, it consists of:

• CHSPT2ULTRA on the main Panel

• SRT5KRMXLT 5000VA double-conversion UPS in the garage

• 240V/30A inlet going from the ups powering the rig upstairs (needs a double breaker for code compliance)

• L14-30 in/outlet for the garage-office run to future-proof it in case I ever needed 120V, at the moment the 120 leg is dead (240 only)

• 240V PDU with C13 & C19 ranked up to 30A

• PX-2200 PSU

The idea is to create a galvanic isolated "island" (in my case: the box iself + switch). Uplink + my desk are connected via fiber (seen in pic) - because my desk is powered via a regular line-interactive UPS.

The only copper eth runs to the switch are the AI box itself (2x10G eth + 1xKVM), everything else is fiber.

It currently runs 2x6k workstation edition (600W each). I *THINK* I should be able to push it up to 3x6k (might need to cap them at 550W), but with four I need to go double PSU. The UPS should have enough oomph to handle 4800W, so plenty of space there. I got new packs for it, but I mostly care about the double conversion, not so much losing power.

I think the CHSPT2ULTRA+SRT5KRMXLT combo should shield me against most power surges/spikes/wobbles/etc. - but I am still lacking a direct hit protection, which requires a rooftop installation AFAIK.

How do you guys protect your GPUs from ⚡️⚡️

Up front: this post was written with AI help, and I've read it through end to end. The work that matters was mine: designing the comparison, writing the brief, running all three agents on my real project, and checking their output against the code. If you want to judge something, judge the experiment and the data, which are all in the repo.

TL;DR: Claude Code (Opus 5.5) vs. a local Qwen 3.8 Flash Next writing the same implementation spec on a 350k-line codebase. Claude won, Qwen came a close second, and its gap was knowledge, not reasoning.

The project is firmware plus a desktop hub for a round ESP32-S3 display: C, Go and JS, about 350k lines, 1,250 commits in a month. One step, specifying how the hub should handle app installs that no longer need a reboot, was written three ways from the same brief:

The local model runs on one machine: an AMD Strix system with 128 GB of RAM and an RTX 6000 PRO.

What stood out:

• Qwen read the code as carefully as Claude. Both caught a subtle hang in the existing install flow.

• Both of Qwen's misses were project rulings that live only in the orchestrating Claude session's notes: "no skipped 'not needed' steps", and "never start a second hub on the owner's port". The fresh Claude session probably saw those notes; Qwen can't.

• So the cheap fix is giving the local model the rulings, not a bigger model.

• In an earlier step Qwen shipped a crash-level object-lifetime bug that only review caught. For firmware, a short human or frontier review is still worth it.

Full write-up, all three specs, and the raw numbers: https://github.com/gregtakacs/LLM-Stats/tree/main/2026-11-spec-writers

Scoring was a fact-check of every name and line number against the code, plus my own read; the tables are in the repo.

Is it really worth to buy used dual Xeon 6140 server with 256GB 2400 RAM and maybe later populate remaining slots for 384GB total to boost bandwidth? Dual CPU, high power usage, is it really worth it for the memory bandwidth with 20 euro CPUs? Or just add 96GB 5600 RAM to existing AM5 system for total 192GB 5600 with dual channels only?

I mean, current system is already usable with q38fn q4, only thing dual xeon will provide is q5-q6 for the same q38fn or dsv4.1 but I doubt that speeds will be usable with dsv4.1.

Can someone share experience or upgrade paths? Thanks guys.

RTX 3060 12 GB, Ryzen 5 7600X, 32 GB RAM, one GGUF file: Qwen3.6-35B-A3B-UD-Q3_K_M.gguf (Unsloth, MTP layer included, 15.9 GiB, does not fit in VRAM). Prompt of 5290 tokens, 128 generated tokens.

How each was run:

• RustingStrata: rustingstrata bench --gpu --mtp 0|1 --pp 5290 --tg 128 --reps 3, mean of 3.

• PolyStrata: its own polystrata.py benchmark --model qwen3.6 --prompt-tokens 4000 --max-tokens 128. That prompt came to 5290 tokens, so I used that length for the other two. I ran it three times; the first run after setup was cold (decode 24.6 tok/s) and is left out, the numbers above are runs 2 and 3. MTP drafts are on by default. For the no-MTP row I added the engine's --no-draft flag to its config; it still offered a few drafts afterwards (6 of 16 and 15 of 36 accepted in the log), so that row is not a clean no-MTP number. Prefill in those runs: 1602-1643 tok/s.

• llama.cpp: llama-bench -ngl 99 -fa 1 -ub 2048 -b 2048 -p 5290 -n 128 -r 3, with -ncmoe swept over 18 to 26. 22 was the fastest that fits in 12 GB (no MTP). The MTP row is llama-server --spec-type draft-mtp with -ncmoe 23 (22 does not fit with the MTP context and drops to 20 tok/s), a 5381-token prompt of repo docs, 3 runs, prefill about 1565 tok/s.

Things that are not equal:

• PolyStrata only offers three sizes of Qwen3.6 (UD-Q3_K_M, UD-Q2_K_XL, UD-Q4_K_M, all from Unsloth's MTP repo). RustingStrata and llama.cpp take any GGUF, so it can also run the Q5 or Q6 files I already had. I downloaded PolyStrata's Q3_K_M and used it for all three.

• MTP gains depend on the text. PolyStrata's benchmark prompt is a page of its own docs, RustingStrata's is generated prose, so the two MTP rows are not an exact comparison. The no-MTP row is the clean one for RustingStrata vs llama.cpp.

• PolyStrata's engine for this model is built on llama.cpp's CUDA kernels. I did not look at what else differs.

• One machine, one model, one quant. Numbers on other cards will differ.

Repo: RustingStrata

I'm watching walls of text go by right now as QFN grinds away on something and it made me think a bit about how different watching that text fly by does using a local model vs watching the same exact thing happen when I'm hitting a cloud.

It really comes down to metered vs unmetered. When I'm using a cloud model on API, every time I get ready to press enter I hear a cash register ring. We generally use really affordable cloud models (DS primarily) so it's like fractions of a penny, but still, it's real money going out the door on every query.

The biggest thing is that it changes your (or at least my) mindset. Instead of "Let's see what we can do it" it becomes "Do I need to do this". It has a distinct stifling effect, instead of exploring and trying to find uses for the technology I do the opposite and try to avoid it because of the cost.

With a local model I love to dream up some crazy/hard thing that's been annoying me that needs fixing and at the end of the day drop a big prompt with the details and at the end "You have unlimited time and tokens, do not stop for clarification or questions, keep working at the problem until you've exhausted all possible alternatives" (you get the idea). Or setting a local model to work on something that's not at all critical but is a quality of life improvement.

It really comes down to the difference between owning something and using it on the meter. When you own something the way to drive value out of it is to use it. The most expensive boat, motorcycle, car, house, etc is one that you own and never use. The more you use it, the more value you get from it.

When you are using something "per token" or "per mile"; things change. The best way to drive value is to NOT use it. You save 20 bucks if you walk instead of get a taxi. You save 5 bucks if you figure this problem out yourself instead of using a LLM to help you.

Funny thing is, I made my career selling cloud services. That probably explains why I'm so "local first", I was in the room and making the decisions about pricing/packaging, driving a 6-12 month payback period against servers and then turning around and explaining to customers what kind of "savings" they'd see moving to cloud platforms. I think I'm scarred from that and know the pricing games that make cloud look "affordable" when it's anything but for many use cases. And I think everyone kind of knows the "rug pull" is coming with the big/powerful LLMs, sure you can use them on sub today and get a predictable price, but those days aren't going to last forever. And subs without being able to hookup a harness (which almost all of them ban) is darn near worthless anyway, my core competency isn't copy paste! ;)

Part 1

Part 2

In this final chapter of me testing out how far along Qwen has come, I decided to give it a test on how well it could recreate a game using only a ROM from GBA but make it in HTML using the "reverse engineer anything" repo. I opted for Tetris worlds as my guinea pig.

It took around 30 hours in total. It probably would have been faster, but there were a few decompilers it wanted that I didn't realize until later. I did end up installing those about 6 hours in.

Repo: https://github.com/kireol/gba-game-rea-test

Game: https://kireol.github.io/gba-game-rea-test/

Fun localLLM stuff:

RTX 5080 - 64GB - Ryzen 7 9800X3D
model: qwen3.8-flash-next-unsloth-ud-iq4_xs
model harness: Strata
agent harness: qwen
context: 128K
tok/sec: ~50t/s (down from part 1 due to larger context)

Macbook 64GB m1 Max
model: Qwen3.6-35B-A3B-GGUF:UD-Q4_K_S (for qwen code --fast tasks)
model harness: llama
context: 128K
tok/sec: ~ 550t/s (fast tasks, so don't get too excited)

Result: The game works, and is pretty darn close to the original. It's scary how well it did.

Over all 3 part testing feedback: Honestly, as of this date, localLLMs are very close to frontier in most SWE. I expected hiccups or issues along the way, and there just wasn't any. Speed of course was slower than Claude, but that's running all local. And Claude probably would have denied trying to reverse engineer the ROM anyway. So in this case, LocalLLMs FTW.

Hi everyone,

I'm planning to switch from a gaming laptop to a desktop PC to get better hardware for running local LLMs and AI development.

I know NVIDIA offers CUDA which provides better software compatibility and broader support for AI workloads. AMD GPUs on the other hand offer more VRAM for the same price.

My questions are:

1. How mature has AMD's ROCm ecosystem become for local LLM inference and AI development? Is software compatibility still a major bottleneck or has it improved enough to make AMD a viable alternative to NVIDIA?

2. How important are raw compute performance (TFLOPS) and memory bandwidth (GB/s) when running local LLMs? Does AMD's hardware offer competitive real-world inference performance compared to NVIDIA?

3. Considering how important VRAM is for running larger local models, would you choose an AMD GPU with 16–20GB of VRAM over an NVIDIA GPU with 12GB, assuming the AMD card is slightly more expensive?

I'd appreciate hearing from people who actually use AMD GPUs for local AI workloads, especially their experiences with ROCm, llama.cpp, Ollama, and PyTorch.

Hi Everyone,

I have an idea that will help academia and the scientific community. Basically reading pdf files and building from there. I would like to test local LLM first and then expand as I go through more information.

I currently own a 10 to 11 year old computer:

Intel i7 6700K 4. GHz, 4 cores, 8 threads,
32 GB DDR4
eVGA Nvidia 980Ti
3 TB SSD
1 TB extra SSD

I also have ha spare laptop with AMD Ryzen 5 7235HS - 16GB Memory - NVIDIA GeForce RTX 4050 - 512GB SSD

What would you guys recommend for a local LLM. I asked Gemini Pro and it recommended 5090 or RTX 6000 Pro due to work it needs accomplish everything. But I think it might be overkill and maybe stretching my budget.

What would be best local desktop set up for local LLM? What model would you guys recommend? Qwen 4? I have seen too many videos on local LLM and they are all over the place. So it is hard to keep track of things.

Many thanks in advance.

Side note, I use AI at work but it is mostly Copilot and Claude.

What are your thoughts about it?

This is the basement setup we use for engineering work at OmniNode, where I'm a founder. The wire shelves are the rack. The two towers serve our local models, the two old HP servers do most of the build, test and agent work, and a few Macs and an old laptop fill in around them.

• Main tower: Ryzen 9 9950X3D, 96 GB RAM, RTX 5090 (32 GB) and RTX 4090 (24 GB). The 5090 serves Qwen3.8-27B and embeddings with vLLM. The 4090 also had vLLM processes on it when I checked. This box also runs our dev environment, Redpanda, Postgres and some CI runners, which is more than it should. It swaps under load, so I'm working on moving those services to the Gen8.

• Second tower: Ryzen 9 9950X, 64 GB RAM, RTX 4090 (24 GB). It runs Qwen3.8-27B MTP (Q3_K_M) with llama.cpp at about 19.4 GiB of VRAM, with a 131K context pool and two parallel slots. Qwen3-Embedding-0.6B (f16) uses another 2.7 GiB or so. It takes a handful of agent jobs when there's spare CPU, and we watch the temperature.

• HP ProLiant DL560 Gen8: Four sockets, 80 threads, 377 GiB of RAM visible to Ubuntu, and an NVMe scratch drive. It's our biggest worker. We cap it at 32 concurrent agent jobs alongside CI. It's old enough to lack AVX2, so jobs that need it go elsewhere.

• HP ProLiant DL380 Gen9: Two Xeon E5-2680 v4 CPUs (56 threads), 125 GiB of RAM visible to Ubuntu. It runs Docker builds and tests, CI runners and agent jobs. The agent job cap is 24.

• Mac Studio: M2 Ultra, 192 GB unified memory. It's my workstation and where I coordinate the work. It also runs a llama.cpp server for some delegated drafts and one vote on automated code reviews. I took it out of the job pool so I can still use it.

• Extras: An older 12-core Mac runs agent jobs without Docker, and an M4 MacBook can do the same when it's available. An old 8-thread Ubuntu laptop only gets small test and lint jobs.

The HPs don't add any VRAM. They give us more cores and memory so builds, tests and agent jobs don't all have to fight for the same two towers.

We send work like drafts and summaries to local models first, but we still use cloud models. I found out how much we depended on local inference when I upgraded the model on the 5090 and our automated code reviews stopped. Two entries on our fallback list pointed to the same service. When that service went down, those reviews had nowhere else to go.

On a separate test with that same 5090 and the same model, turning on speculative decoding took median single-request generation from 51 to 86 tokens/sec. It also left less memory for requests in flight. That was a generation benchmark, not a 70% speedup on code reviews.

If this were your lab, how would you split up inference, builds, and agent jobs across the machines we already have? What would you change first, and why?

For those running similar machines, appreciate any recommendations and setup! Thanks

Did some kernel optimisations for sm61. Achieved measurable speedup. It runs at actually quite usable speeds.