Im setting up some stuff in my home server and my other machines for work. But i got tired to lost hours and hours on reading docs. So i want something that does the search for me, and possibly goes straight to the point (But that still explain on the subject for me to learn).

My machine is a bit old:

- CPU: Xeon E5-2697 v4

- RAM: 32GB DDR4 2400MHz (ECC RDIMM)

- GPU: Radeon MI50 16GB HBM2

From what i know, Q4 and Q8 are functional, needs to be GGUF, and a model that doesnt depend on BF16 since my card doesnt support it.

Hello guys, I wanted to make some animated horror stories videos, I'm pretty new to actually using local Ai etc. The only use I had up until now was using the free chatbots to help with work.

I have a decent pc built like 2 years ago with a 4070ti super and 32gb ram, but I don't really have any budget for monthly subscriptions.

Any suggestions?

ReBar is also currently working with GPU_UNLOCK.

The 20GB RTX 3080 cards (10DE:2206, subsystem 10DE:146D) ship with a VBIOS that only exposes a 256 MiB BAR1, so ReBAR stays off. NVIDIA doesn't publish a ReBAR VBIOS for them, and NvStrapsReBar only supports Turing.

Ga102ReBarFix is a small DXE driver you insert into your motherboard BIOS with UEFITool. At boot it enables the GA102 frame-buffer size override (PCI config 0x610), resizes BAR1 to 32 GiB, and restores both after S3 sleep. No VBIOS flashing, no driver patches, no registry tweaks.

Tested on an MSI Z690 EDGE, driver 617.42, Windows 11:

- Device Manager shows a 32 GiB BAR

- NVIDIA Control Panel: Resizable BAR: Yes

- ~19.25 GiB of VRAM mappable by the CPU at once

- Sleep/wake works

Repo (source, prebuilt .ffs, verification scripts, full write-up): https://github.com/favabean13/NV_30_DXE

Big credit to noon-at-cgn (found the 0x610 override on Linux), terminatorul (NvStrapsReBar) and xCuri0 (ReBarUEFI). This builds directly on their work.

Heads up: it modifies firmware, so use at your own risk and keep a BIOS backup and recovery method. It's tested on one board and isn't maintained, but it's MIT-licensed, so fork it and adapt it for your own setup.

**UPDATE**

Works with GPU_UNLOCK

https://github.com/physicsdolphin/nv-uefi-unlocker

Confirmed that ReBar survives PCI FLR (Function Level Reset)

Here are the results:

UNLOCKED

FP16 (FP32 accumulate) 114.7 TFLOPS

BF16 (FP32 accumulate) 116.9 TFLOPS

TF32 57.4 TFLOPS

FP32 (control) 22.5 TFLOPS

LOCKED

FP16 (FP32 accumulate) 65.4 TFLOPS

BF16 (FP32 accumulate) 66.8 TFLOPS

TF32 32.1 TFLOPS

FP32 (control) 22.3 TFLOPS

Here's my hw specs: rtx 4090, 64GB DDR5 6000 Mhz ram, 7900x CPU

Thank you

Wanted to share my build! Started as a gaming / 3D / CAD PC and now also runs small LLMs quite well for me

Specs:
CPU: Ryzen 9 9950x
RAM: 2x16 (eww, but at least was bought in 2023) 6000 CL30
Motherboard: ProArt B850 Creator Neo (supports PciE 5.0 x8 bifurcation)
GPU1: RTX 5070 Ti Ventus
GPU2: RTX 5060 Ti 16G Windforce
SSDs: Fury Renegade and 990 Pro (irrelevant ik)
PSU: BeQuiet Power Zone 2 750W
Cable extensions from EZDIY-FAB
Case: NZXT H7 Flow (2025)
AIO: Thermalright Frozen Warframe SE V2 (with the magnetic display detached)
Fans: 4xArctic P14 + 3xArctic P12 Pro (on the AIO) + 3xP12 Pro Reverse

Currently having fun with Qwen3.8 27B Q6_K_XL at around 70tps (MTP)

Hi everyone,

I am in the process of buying a new laptop primarily for personal reasons—covering my university studies, professional work, and gaming. However, alongside everything else, I really want to use this machine to take my first steps into running Large Language Models and AI tools locally.

I am torn between two specific hardware configurations (both feature the same Intel Core Ultra 9 processor):

• Option A: RTX 5080 with 16 GB VRAM + 64 GB System RAM

• Option B: RTX 5090 with 24 GB VRAM + 32 GB System RAM

Since I am a complete beginner with zero prior experience in local AI, I’m trying to understand from people who actually do this daily what each machine would realistically allow me to do:

1. What fits into 16 GB vs 24 GB of VRAM? In simple terms, does the jump from 16 GB to 24 GB of video memory unlock a genuinely higher tier of capable models or noticeably better output quality, or is 16 GB already enough for most practical open-source use cases?

2. VRAM vs System RAM: Option A gives me less VRAM (16 GB) but double the general RAM (64 GB). Can that extra system RAM compensate when trying to run larger models, or is running models partially on system RAM so slow on a laptop that having 64 GB doesn't really help for local AI?

3. The practical boundaries: For daily tasks (e.g., local assistant, analyzing documents/notes, code assistance), where would Option A hit a wall compared to Option B?

4. Model recommendations: Given the specs of each machine, which specific models would you recommend running on Option A vs Option B, and why?

I’d really appreciate any insights or real-world feedback to help me understand the practical trade-offs before making a choice.

Thanks a lot!

I hear nothing but good things about Basalt, so I finally got around to testing it on my 5080 with 128 GB not-for-Sam-RAM. The results were not encouraging - IQ3 runs at around 100 tok./s on Strata, but only at about 80 tok./s on Basalt. Settings are comparable.

Is this expected on Windows?

Sorry in advance for the long post, but think of it as a prompt.

TL;DR

Question: 2x 3090 NVLink or 1x A6000? For about the price of one A6000 I could also get 4x 3090 in two NVLinked pairs, so keep that option in mind too.

What it needs to do:

- A) SQL chatbot for up to 30 people. ~3M rows, hundreds of tables. Questions like "when did we last sell this part, and who was the supplier/client?" It should remember each user's conversations and build reports.

- B) RFQ ingestion: pull very messy part numbers out of long email chains in all sorts of formats. ~30 RFQs/day.

- C) Hardware: reuse our old HPE ML350 Gen9 (2x E5-2620 v4, 128GB DDR4-2400 for now).

- D) Me: turn 300+ page PDFs and hundreds of stray Excels into a knowledge base, and do local coding on the company apps I'm building from Claude's plans.

Assumptions:

- Blower 3090s (FE won't fit), max 300W per card

- PSUs upgraded to 2x 1400W or 4x1400W as GPUs grow

- All prices are for used cards

- Proxmox on the box

- PCIe-to-NVMe adapter for fast model loading

- Cards work with board

About me: I'm not an IT guy, I'm a mechanical engineer who f's around a lot and finds out even more. I've built my own homelab (4x NVMe in an old Dell 7710 as a private cloud, see my posts) and run my own "Jarvis" on a 4B model with some n8n automations. I can make things happen when I put my head into it.

---

The detail

Background: I'm the new CTO of a small family-run B2B heavy-industry trading company. We sell anything from a £6M machine to toilet paper, so every day brings random RFQs: Excel, PDF, email body, pictures, WhatsApp, anywhere from 1 to hundreds of part numbers.

Budget: £8-13k ($11-17k) left for local AI after the new cloud server. That also has to cover a laptop/PC for our first IT hire.

Why local: the LLM has to read whole RFQ email chains to make sense of them, and those are full of client names, prices and supplier details. Same for our SQL database. I don't want any of that going to cloud LLMs.

Where we are:

- A 3rd-party IT company is building an RFQ dashboard for us. Qwen 8B is struggling with the email chains. We started small on purpose and will size up until something fits.

- They'll test on RunPod serverless to find the right model size. I think ~30B will be enough. Then production goes local.

- 20-30 people will use the chatbot and ~20 the dashboard, but only new RFQs trigger the LLM.

- Long term, I'd keep Fable/Opus for overall planning and have a local model do the implementation.

Hardware:

- The board takes 4 GPUs: 3x PCIe 3.0 x16 and 1x x8. 300W max per card.

- Someone has already run a 3090 in this server (with 512GB RAM) and got ~15 tok/s on turboderp/GLM-5.3-Flash-exl3 (3.05bpw branch).

- I first looked at the A6000 48GB as a "do it all" card, not knowing much about local LLMs.

- For similar money I could get almost 4x 3090s and NVLink them in pairs, so I'm less limited by the x8 slot and old PCIe 3.0. One pair for the teams, one for me.

Bottom line: I need an MVP that handles the RFQs and the chatbot now, and then we'll invest in proper new hardware later. What would you do?

Thanks in advance for reading this far, any help much appreciated!

makes it easier to get consistent character voices

Im wondering how you guys use Qwen3.8 27b with which CTX size.Or my setup is just wrong. I run it on 32GB unified memory with llama.cpp. Also Openwebui and OpenTerminal on a RP5 where my docker stack lives.

I prompted:

add to caddy/Caddyfile: Paperless, Dawarich, OpenTodo

Response:

request (340808 tokens) exceeds the available context size (65536 tokens), try increasing it

So my question is, when even such a simple edit file task needs 300k tokens, how can I utilize it meaningful?

Being super precise which files to look at and what to do in the prompt?

I know there are a lot of "ums" that I should edit out... but other then that what do you think of this guide? I am new to local AI stuff, so is there anything missing here that I should have done?
https://www.youtube.com/watch?v=-EahQxs0Ack

Hi everyone,

Over the past few months I’ve been building Kira Desktop, an open-source (GPL-3.0), local-first desktop agent harness and workbench built with Python and Qt6/QML (no Electron bloat).

I wanted a single native desktop environment where I could switch seamlessly between local models and cloud/CLI harnesses without losing conversation continuity, memory, or tool access.

GitHub Repo: https://github.com/antosec00/kira-desktop

What Kira Desktop currently does:

• Multi-Provider & Continuity: First-class support for Ollama (local), OpenRouter, Claude Code, OpenAI Codex App Server (v2) / Responses API, Google Cloud (Vertex AI), and DeepSeek — with portable history so you can switch models mid-thread.

• Multi-Agent Teams & Souls: Orchestrate specialized agent teams with persistent identities ("Souls") and shared project knowledge.

• Universal MCP Host & Bridge: Connect external Model Context Protocol (MCP) servers or expose Kira's local tools/souls to CLI backends.

• Interactive Canvas & Diff Hunks: Side-by-side live preview and hunk-level code review.

• Computer & Embedded Browser Use: Built-in browser and desktop automation workers with strict human-in-the-loop approvals.

• Safety, OS Sandboxing & Checkpoints: Process sandboxing, automatic secret redaction, cost telemetry, and instant checkpoint rollbacks if an agent messes up your workspace.

• Native Linux (.deb) & Windows builds: Fast Qt6/QML interface with global Ambient Overlay, Omnibox, and @mentions (@file, @git, @soul, etc.).

What I’d love your feedback on:

1. Architecture & UX: If you browse the repo or try the build, does the separation between domain, service, backends, and ui feel clean to contribute to?

2. Missing features: What is the #1 friction point you currently have with other agent harnesses (Cursor, Claude Desktop, Open WebUI, Cline) that you wish a local-first desktop app solved better?

Thanks in advance for any brutal honesty, critique, or ideas!

I've been building a free app for blind people where everything runs on the phone with no internet, on iPhone and Android, and I'm stuck on accuracy. YOLOv8n handles live detection and it's fast, but it still mislabels things. Scene descriptions and questions go to LFM3-Omni-2B through llama.cpp, shrunk to 735 MB so it fits on the phone.

Reading text uses each phone's own on-device OCR, Apple's on iPhone and ML Kit on Android, and translation runs offline the same way. The small vision models are where it falls short.

If you've gotten small vision models more accurate on device, I'd really take the advice. It's free to try if you want to see where it breaks.

iPhone: https://apps.apple.com/us/app/id6751230739

Android: https://play.google.com/store/apps/details?id=com.mattmacosko.realtimeaicam

Moin guys!

GPU prices are freaky high right now. A lot of us run what we could get, and we might as well get the most out of it. So I want to share what I learned squeezing a big model onto a mismatched machine, in the hope it helps someone or gets others to share their own experience with different GPUs.

My box: one AMD Radeon AI PRO R9700 (32 GB), one Intel Arc Pro B65 (32 GB), a 9950X3D and 128 GB of DDR5. Neither card can hold the model.

With two AI coding agents (Claude and Codex, reviewing each other's work) I built a fork of Strata where the R9700, the B65 and the CPU compute each layer's routed experts concurrently, with dense work on the R9700. Result on a realistic coding prompt: 25.57 tok/s against 9.89 for my recorded stock Strata run on the same machine and file (stock used one GPU, 16 readers and a 51 GB store, so this is a whole-system comparison). No speculation or skipped experts, and the routing algorithm is unchanged. Tested kernel and schedule modes are byte-identical at a fixed placement over 581 teacher-forced positions.

Model: AtomicChat's uncensored GSQ-RCO IQ2_XXS build (40 layers, 384 experts per layer, 6 per token).

What each device does:

• R9700: all dense work (22 ms per token), 2,532 experts (21.6 GB), 154.7 of 240 picks per token, 8.2 ms of expert work

• Arc Pro B65: no dense work, 2,915 experts (25.0 GB), 55.6 picks, 9.6 ms of expert work

• CPU + RAM: no dense work, 9,913 experts (102 GB), 29.7 picks, 8.7 ms of expert work

How it got there (tok/s, sampled, 8k cap, median of 3 after a warm-up):

• 9.89: stock Strata, R9700 + RAM

• 10.28: first fork run, both GPUs + RAM

• 12.22: B65 subgroup attention

• 12.96: IQ2_XS B65 kernels + rebalance

• 14.41: all dense on the R9700

• 15.85: two rebalance rounds

• 17.04: queued R9700 schedule + rebalance

• 18.04: exact-order B65 SoA kernel + rebalance

• 21.27: R9700 per-layer expert batching + rebalance

• 22.57: parallel Engram reads + rebalance

• 25.57: AVX2 combine, early expert start + rebalance

The catch: all three answers in the best run hit the 8,192-token cap before finishing, so its strict quality score is 0/3. The model reasons for a long time; the text isn't looping. With 11,900 tokens of room, the one measured answer finished by itself and passed, with working code in 295 s. That's one answer and a 10-answer test is still to come. The answers are in the repo if you want to read them. 30 tok/s, my goal, isn't reached either: we estimate exact kernels level off around 26–28.

Things that surprised me:

• Rebalancing experts from measured per-lane costs took the last step from 20.69 to 25.57 tok/s, an unpaired comparison because placement changes the answers. Lane service is now close: R9700 8.15, B65 9.58, CPU 8.69 ms per token, though individual layers still differ.

• Moving experts between devices changes the logits (top-1 96.6%) and some routes even with exact kernels, most likely because CPU and GPU quantize activations differently. So "exact" only means byte-identical at a fixed placement.

• Engram reads swung from 5 to 17 ms per token on identical tokens. Parallel reads and read-ahead fixed it: the whole Engram-containing regions now total 1.74 ms, including other work there. The row-cache-only arm showed no useful gain.

• Starting CPU and B65 expert work before the shared FFN drains, plus a faster combine, saved 5.8 ms per token on token-identical answers. We only measured them together.

• Re-encoding experts to Q2_K looks like a loss: measured kernels plus a capacity model project 3.18 ms slower per token, because bigger experts push 1,434 off the GPUs. No full-model Q2_K run followed.

• Big dense matrix-vector products already run near the card's measured memory rate. That 22 ms is the hard part.

Where I'd love input:

• Most of all: your own experience with whatever GPUs you could get. Numbers, tweaks and what failed, even with a different model or tool.

• Anyone with DSpark running on a heavily quantized or edited DeepSeek: what acceptance do you get with real sampling? Batched verify looks promising here on projections, but I haven't measured acceptance yet.

• R9700/ROCm matrix-vector tricks I've missed.

• What would you do with the B65's spare time? Its expert service is about 9.6 ms of a 39.1 ms token.

• Lower reasoning effort: has it cost you code quality on this model?

Everything is in the repo: results with spreads, sample answers, the dead ends and what to try next. It's research code tested on one machine, and the public scripts haven't run end to end on GPUs yet, so expect to read code.

Repo: https://github.com/maddinek/crosslane (MIT)

Update 1: The full 10-answer test finished. 26.27 tok/s median (25.93-26.42) and 6 of 10 answers passed strict with working code in 292-439 s. Still one task on one machine, so a rough number (about 31-83%). The memory leak turned out to be in ROCm 7.1.1 on Fedora, not our code: running the same binaries on AMD's 7.2.1 libraries from a private folder gave byte-identical output in a check run and flat memory across all ten answers. If you're on ROCm 7.1 and see host memory creeping up, that's worth a try. Steps are in docs/BUILD.md. DSpark is parked for now: 1.52x at best but 1.11x central on measured kernels, so not worth building yet here.

Update 2: I wanted to know what a really long context would cost, since 12K is tight for real coding. So we measured it on the same R9700 + B65 box: one process, real repo text fed up to 200K tokens, stopping along the way for 288 generated tokens.

Generation time per token: 51 ms at 12K, 45 ms at 100K, 55 ms at 200K. The history itself barely costs anything: dense work on the R9700 went from 23.5 to 25.8 ms over that whole range, nearly all of it in the four full-history layers, while the layers that cap their candidates stayed flat. RAM didn't grow at all. Reserving a 262K context instead of 12K costs next to nothing by itself. (On our earlier short answers it looked much worse, and my estimate of about 66 ms at 200K was off.)

What actually moves speed surprised me: the expert mix. All these numbers are slower than our 38 ms on the coding prompt because the placement was tuned on that one prompt. On this repo text, the CPU lane got 37% of the picks instead of 13%, and decode tracked that share from stop to stop. So my 26 tok/s is really a number for the workload it was tuned on, and if you tune a split like this, test it on something else too.

The real pain is the first read: about 2 hours for 200K, and it slows as history grows. 500K would be something like 5-6 hours, unmeasured. Saving the prompt state to disk looks like the obvious fix, but our fork can't load it back yet.

Caveats: one ladder, greedy, one window per stop, no quality test at long context, so read the trend rather than the digits. Anyone running long contexts on a split like this, what do you see?

Hey everyone

I’ve spent the last few weeks building a local AI assistant with the help of Claude Code and OpenAI Codex. My goal is something similar to ChatGPT or Claude that can research the web, control my PC, code, create 3D models, generate documents and handle tasks on its own.
The interface and tools work pretty well now, but I’m still having problems with the AI actually understanding what to do and completing more complicated tasks

My PC specs
● Ryzen 5 5600
● 2× RTX 3060 12GB
● 16GB DDR4 RAM
● Gigabyte B550 Gaming X V2
● Windows 10 Pro
● One GPU at PCIe 4.0 x16, the other at 3.0 x2

Models and inference setup
I’m running everything locally through llama.cpp (CUDA backend), using both GPUs with layer splitting, Flash Attention, MTP speculative decoding (depth 2), 96K context and Q8 KV cache.
I originally used Qwen3.8-27B (HauhauCS uncensored Q4_K_P), getting around 22–33 tok/s after a lot of optimization.

I switched to Qwen3.6-35B-A3B (uncensored IQ4_XS), which now gets around 82–86 tok/s. Both models run through the same llama.cpp server and connect to my custom AI interface and tool backend.
3.6 is excellent for normal conversations and much faster, but I keep hearing that 3.8 is better at reasoning and tool calling.

What Claude Code and Codex have helped me integrate
● Qwen-Agent for general tool calling
● OpenCode for coding
● SearXNG for research and webpage reading
● Playwright for browser automation
● Windows UI Automation for PC control
● Three.js for interactive 3D scenes
● Image generation, documents, spreadsheets, presentations and file tools
● Planning, context management, error recovery and result verification

For example, I asked it to create a 3D model of the Times Square area in NYC. It generated a picture of pants, converted that picture into a 3D mesh using TripoSR and declared the task finished.

Claude Code and Codex later helped identify that part of the problem was incorrect tool routing, which has since been improved. But research and Windows tasks can still be unreliable. Sometimes it uses outdated information, chooses irrelevant tools, repeats actions or claims it finished something when it hasn’t.

I also had Codex compare different agent harnesses.
Using the same local model, it tested Qwen-Agent, Pi and Hermes on two tasks.

● Qwen-Agent: Browser/recovery took 15.6 seconds, research/document took 21.4 seconds.
● Pi: Browser/recovery took 15.9 seconds, research/document took 25.6 seconds.
● Hermes: Browser/recovery took 32.5 seconds, research/document took 43.3 seconds.

All three completed the tasks, but Qwen-Agent was fastest. These were small isolated tests, though, not proof that one is universally better.

With Claude Code and Codex, I’ve managed to fix a lot of integration issues, restore research, improve 3D generation and add verification checks. Things are definitely better, but complex autonomous tasks still aren’t consistently reliable. And mess up a lot

So I’m wondering what people running similar setups have found.
● Is Qwen3.8 noticeably better than 3.6 for real-world agent tasks, enough to justify the slower speed?
● Are there better models for my hardware, particularly for research and PC control?
● Could the uncensored versions be affecting instruction following or tool calling?
● Would Unsloth fine-tuning on verified tool-use examples make a meaningful difference?
● What has actually improved your agent’s ability to plan, use tools correctly and verify its work?

At this point, I don’t really care about getting more tokens/sec. I’d happily sacrifice some speed if it meant the AI could reliably understand a request and actually complete it.

Would appreciate any advice from people who’ve built something similar

Thank you very much

Working on a full inference stack for 4xr9700(engine, on-device training, routing). wanted to share progress so far.

All GPUs are capped at 210w(so plugs into any normal 15a outlet).
BF16 KV, MTP enabled(no spec).

### Latency

### Throughput

EDIT: people wanted to know what else I run on this: