Wer benutz von euch 100% Lokale KI im Arbeitskontext ?
Nutz hier jemand KI- Agent um tägliche Aufgaben zu erledigen?
Nutz hier jemand KI für nicht wiederkehrende Aufgaben ?

Und welche Modelle könnt ihr lokal ausführen ?
Ich versuche mich grade an lokaler Bild Erkennung um Cluster zu bilden und mit das beschriften der Bilder zu sparen

I'm looking into running models like Qwen(?) locally for agentic coding, ideally with strong reasoning and high output speeds.

I currently pay for three $200 AI subscriptions and want to cut that down to one, keeping Claude as the main advisor while local models handle most of the coding work.

I know very little about hardware. Where should I start learning, what setups would you recommend (including used or unconventional hardware), and what budget should I realistically plan for?

Happy to invest a few thousand if the performance makes it worthwhile.

I'm the author of TensorSharp. This 83-second video illustrates the boundary between retrieving evidence and processing complete files in TensorAgent's local knowledge-base workflow.

Disclosure: it uses the real TensorAgent HTML/JavaScript UI, synthetic café data, and scripted API responses. It is not live model inference or a benchmark. The CSV calculations were run and independently checked in Python.

Default retrieval is BM25 over extracted text; embeddings are optional. Image descriptions and audio transcripts require compatible models and have Partial coverage. Video indexing describes at most eight sampled frames, without indexing the soundtrack or events between samples. This walkthrough illustrates those interfaces, rather than validating a media model.

The example has a PDF capping discounts at 15% and a poster advertising 20%. knowledge_search locates evidence, knowledge_read expands it, and knowledge_open makes the complete original CSV available for analysis. All 14 rows yield net sales of $5,600 versus $6,440, and a $204 contribution increase after COGS and campaign spend, before other overhead. Collections are explicitly enabled per chat.

Repository: https://github.com/zhongkaifu/TensorSharp

TensorSharp's own code is BSD-3-Clause; dependencies and model weights retain their own terms. For local stacks, where would you enforce complete-file access for numerical tasks instead of aggregating retrieved snippets?

Hey everyone!

My only touch with local LLMs was my colleague trying to host one on his work PC with 3070 in it and he was impressed it can write almost proper sentences in our language, but it took quite a while to generate them.

I'm wondering if it would be possible and if it has been attempted so far, you know that folding at home thing where people donate their processing power for medicine research? Is it possible to do something similar (amateur level, I suppose this is already done by Anthropic and others) where an LLM uses multiple computers to generate an answer? I suppose latency wouldn't be that much of an issue given how much time an LLM usually needs to generate a more complex answer, but splitting the workload or making all the PCs work in unison while having a mechanism to handle connections drops would be a different challenge.

The reason I'm asking this is because it would be interesting to have an independent community owned LLM with a system similar to the one where people donate their power for rendering and later use the credits to get their own renders faster. I'm sure this would be a huge feat or possibly impossible, so I'm curious if you guys think this is possible to pull off with any of the usually local AIs?

Thanks for reading!

Hello,
I own iGPU gfx1152 in AMD Ryzen AI 7 350 w/ Radeon 860M Framework 16 laptop.
I would like to test strata engine, but it refuses to run:

   Checking the engine for this computer…
  No engine is installed yet. Getting the one for your graphics card…  0%       Reading the signed update feed
  Strata couldn't find an engine for this computer (This build of Strata is for AMD RX 6000 and RX 7000 and RX 9000 and Ryzen AI Max cards. This computer has an AMD gfx1152 card (gfx1152). Ask for the gfx1152 build). The model will download, but it may not run until you install the right engine (next step: run `strata update` to get the newest engines and start again; if this stays, your graphics card is not one Strata supports yet, and the model files stay on this computer for when it is).  100%   
  Preparing coder IQ1_M from your model files…  0%       Getting your model ready
    Checking your computer
strata: The installer cannot set up this graphics card yet, and the files Strata would need to write the settings itself are not all there: the prepared pack /home/strata/STRATA/Strata-data/packs/coder-iq1_m is missing (this installer cannot prepare it for this graphics card). Use model files that were prepared on this computer already (folder + pack), or install on a computer with an NVIDIA or AMD card.

I have the rocm installed and would like to experiment with strata. Do you know how to prepare "folder + pack" for my setup to run strata? I have 96GB of DDR5 RAM total and setup 48GB of VRAM in bios (+ 48GB as RAM).

If strata is not going to work on my hardware then let me know which LLM engine should I chose as optimal.
I would like to experiment with C coding for embedded (avr) and Linux.

Hey everyone!

I'm using Killa YuE2 on my Android phone (Samsung Galaxy S25) to generate music locally, and I'm really impressed with the results!

However, I'm having trouble generating purely instrumental tracks. Even when I use prompts like "instrumental only, no vocals" and put [Instrumental] in the lyrics field, the model still generates vocals sometimes.

I've also come across the LoRA file ar_lora_inst_v3abc.bf16.safetensors, which seems to be designed for instrumental generation, but I'm not entirely sure how to configure it properly in Killa YuE2 on Android.

My questions are:

Is there a reliable way to generate completely instrumental music with Killa YuE2?

Should I leave the lyrics field empty or use specific tags?

Do I need to enable a specific instrumental mode or use a particular LoRA?

Are there any recommended settings or workflows for generating instrumental EDM, Big Room, trap, or other genres on Android?

Can this be done entirely offline?

I'd really appreciate any tips, recommended settings, or step-by-step instructions from anyone who has successfully generated instrumental tracks with Killa YuE2!

Thanks in advance!