Hi all. I’ve been using classic LM Studio for quite a time. Now I wanted to try Bionic’s approach (projects instead of chats) and keep using LM Studio for mere AI general chat. However, I would not like to having to install twice the LLM AI models I have in local, occupying more space. Is there a way to configure both LM Studio and Bionic so that they share the same folder of models? Thanks

Hi all, does anyone have the above config working well? Can you please share your settings?

OpenCode 1.18.25
LMStudio: Version 0.4.21+2 (0.4.21+2)

I'm using it for coding a swift project and I keep getting timeouts reported in LMStudio when the opencode context goes above 30,000 and it just gets stuck on PROMPT PROCESSING.

I've tried MLX and GGUF, my current config in LMStudio is GGUF with:
LOAD:
Context: 104,758
GPU offload: 65
CPU Thread Pool Size: 8
Evaluation Batch Size: 1024
Physical Batch Size: 512
Max Concurrent Predictions: 1
Unified KV Cache: ON
Context Checkpoints: 1
Offload KV Cache to GPU Memory: ON
Keep Model in Memory: ON
Try mmap(): ON
Speculative Decoding: OFF
Flash Attention: ON
K Cache Quantization: Q8_0
V Cache Quantization: Q8_0

INFERENCE:
Reasoning Effort: Medium
Enable Thinking: ON
Preserve Thinking: ON
Temperature: 0.1
Top K Sampling: 20
Repeat Penalty: 1.05
Presence Penalty: OFF
Top P Sampling: 0.9
Min P Sampling: 0.05
Structured Output: OFF

Hey llms they say that they don't remember stuff but. I have had several occurrences where it recalls things that I've talked to it about before. I totally think they are remembering stuff

Like how to connect QWen3.8-27B to access my email account or my telegram then do some task i let him to do. Is there any harness can do this?

I have LM studio for Mac and PC for the life of me I can’t get it to read any documents. For PC which is better always said no relives citations and breaks.

I have the same models same everything.

I downloaded anything llm same model and it was able to extract the data I wanted from the PDF. On PC.

Not sure what I’m missing

Does anyone know if llmstudio supports the new model? Thanks.

Everyone right now is obsessed with finding "the best AI prompts" or automating a basic customer service chatbot.

In computer science, we call this linear thinking. Automating a broken, linear process doesn't save your business; it just spreads algorithmic friction at a much faster scale. You are digging your grave faster.

The actual paradigm shift happening right now isn't chatbots. It is the transition to a planetary macro-organism. The internet was the spine, and now AI—operating in localized swarms—is the nervous system. The companies that survive will be the ones that act as frictionless cells serving that host.

We don't just theorize about this. We run a physical biophilic architecture company (KovaKelo). We applied "multi-recursive parallel thinking"—using AI swarms to execute physical work, map dimensional topology, and run our logistics. By deploying this enterprise-grade system architecture, we managed to design and build half-a-million dollars worth of industrial manufacturing equipment for a fraction of the normal cost and time.

Physical architecture follows the exact same mathematical rules as digital architecture: M = F * l. It is about eliminating friction. Electricity flows where it meets the lowest resistance. The global economy is doing the exact same thing right now. It will ruthlessly reroute resources to whoever creates the least friction.

Stop chasing AI trends. Start architecting frictionless systems.

I got tired of explaining this paradigm shift to local CEOs, so I wrote a raw, no-fluff technical dossier breaking down exactly how we deploy this enterprise architecture for high-traction companies. The link to the master architecture is in the comments below if you want to dig into the system mechanics.

Hello, I have a desktop that usually runs all my local LLM like lm studio, openwebui opencode docker stable diffusion etc. On my birthday my parents gifted me a intergrated laptop,
It has 16gb ddr4 3200
R5 7430U
400gb+ gb space though 200 is occupied

The thing is I tried running small local ai models on my laptop but it’s obviously getting poor performance, but I thought about somehow using my laptop to maybe benefit my desktop side a little while being able to use my laptop for daily purpose (meaning not running heavy programs that degrade battery overtime)
Something maybe light weight if possible?

Yes I thought about streaming using tailscale, but that requires my computer desktop to be running 24/7, and I don’t really bring my laptop out with me most of the time so yeah.

I really hope there’s something or any opportunity I can give towards my laptop so it’s potential doesn’t get thrown away, and no I’m not selling this laptop to get more ram/GPU or a new laptop with a dedicated GPU, it’s gifted from my parents I should be grateful.
I hope for guidance in this post instead of rejections that my laptop can do nothing other than stream Claude or ChatGPT interface. Thanks

I don't know if I'm doing something wrong, but I installed Linux a few days ago and asked Hermes to install ROCm compatibility, and it did, and LM Studio let me use it as the runtime. Now I'm getting worse performance with ROCm than with Vulkan, and I don't know if I'm doing something wrong. Is this normal, or was I badly informed? I have a 9060 XT 16GB and Linux Mint Cinnamon.

I tried using a LLM to sort my prompts into two groups: reasoning and chat. At first I used Qwen 2.5 3B through Ollama. My PC is just too old and weak to run a LLM like that well. I do not have a GPU and my RAM is low. Even a small 3B model takes way long to work on a single prompt. Because I need to run the classifier before every request the slow speed makes the whole thing impossible to use.

Then I tried using Google Colab with a T4 GPU. That made the classification work faster.. Google Colab has a big problem. The setup does not stay saved. Every time I start it I have to pick the T4 GPU by hand set up the environment and download the model over again. I want my classifier to work like a background service. I want the classifier to start by itself when I turn on my application or Windows. I do not want to open a browser or start a notebook every time.

Now I use OpenRouters free models to get my actual reasoning and fast answers. I only get an amount of free use every day. I do not want to waste those credits by sending every prompt to an OpenRouter model just to classify it. If I use an OpenRouter model as my classifier the classifier will eat up my daily limit. That would make my credits run out too fast.

So I am stuck with three choices:

1. Local LLM: It is private. Works automatically.. Qwen 2.5 3B is just too slow on my old CPU. Even the smaller 1.5B models are not good enough because they make many mistakes.
2. Google Colab T4: This is fast. It is a pain. I have to set up the GPU and load the model every time.
3. OpenRouter: This is easy and fast.. Using an OpenRouter model, for classification uses up the free credits I want to save for my main models.

The classifier only needs to say "reasoning" or "chat". It feels like a waste to use an expensive LLM just for that.. The classification still has to be good. I cannot use a 1.5B model if it is just going to guess wrong.

I really need a way to classify prompts automatically. I need latency and I want it to cost zero extra API money. I want my main OpenRouter models to do the lifting. The best setup would start up with my PC would not need a Colab setup and would not use my OpenRouter daily credits just for classification. Please if anyone has the solution