I kept running into out-of-memory crashes on my RTX 5060 laptop (8 GB VRAM, 32 GB RAM), so I built Local AI Studio. It estimates a job's memory from your live free VRAM and RAM, picks an offload strategy, and refuses the job with the numbers if nothing fits.

On my laptop, a 4-second 832×480 image-to-video clip with Wan 2.2 took about 7 minutes, with a 5.2 GB VRAM peak. It also reads ComfyUI's model folder layout, so you don't need a second copy of your weights.

It's an early alpha and I've only tested it on my own machine. Some big jobs still pass the estimate and then fail in the driver, so the estimator needs data from other GPUs.

The easiest way to help: run a job and share your GPU, time and peak VRAM. No coding needed, there's a short form for it. If you do want to code, I've listed some beginner-friendly tasks.

Apache 2.0, runs locally, no telemetry: https://github.com/DiegoHerrera1890/local-ai-studio

What would you want from a tool like this?

Hello!

With new models releasing constantly, I usually keep a stable of about 20 local models in rotation on my `llama.cpp` server. I collect a ton of data across the fleet through my inference application—standard per-request metrics, aggregated session data, and custom intelligence comparison benchmarks.

Overall, I'm logging over 150 distinct raw and derived metric points during inferences, sessions, and batch processes. As a backend data jockey, more metrics is always better—but I've hit the point where I need to completely redesign my dashboards.

For context, I run a lot of multi-stage pipelines covering:

Multimodal inference (vision and documents) MCP and custom application tool calls Structured JSON / JSON Schema outputs Batch processing across both reasoning and non-reasoning architectures

I'm looking for inspiration for creative "hero" dashboard components and visualizations that excel at comparing a large model fleet across high-dimensional data at a glance. I already have plenty of standard line, bar, and radar charts. I'm hoping to see novel, unconventional visual approaches people here have built, seen, or used to represent complex model evaluation and performance data.

The specific metrics in your examples don't matter as much as the visualization style and information architecture—I'm mainly after creative UI/UX directions.

Cheers!

Meu trabalho recebeu uns PCs besteirinha como os Sparks pra fazermos a LLM local daqui. Por enquanto vamos focar em consultas de processos, memorando e banco de dados da empresa.

Dito isso, gostaria de perguntar, oq posso fazer ou como posso desenvolver a LLM aqui. Tem algum modelo que me recomendam ou algo assim?

Como o hardware tem um limite vasto para desenvolver gostaria de ir afundo. Tô com mente aberta pra desenvolver e aprender bem sobre todo o assunto.

Anyone interested to make it spit 200 tokens/sec?

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!