What tools and projects do you use for your local LLM chats and agents?
I’m trying to sift through the mess of LLM harness and tools to find how to really get the most out of local LLMs.
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Currently, I’m using Hermes and OpenCode mostly, backed by Ollama running a couple of different models. Usually I’m running the Gemma4 or Gwen3.5 models.
Memory management is done through Obsidian as every harness seems to have really good skills for it.
Pi Coding Agent on the laptop, openwebui as a self hosted service.
I don’t host LLMs myself. My best hardware has 16GB of RAM, I could run a 4B model or so, but what’s the point? So I buy compute with ZDR turned on.
The anti-AI sentiment here is ridiculous. They were asking a legitimate question!
I just use ollama locally but I keep meaning to setup OpenRouter because I like to play with larger models that my hardware can’t handle and I’d like to be able to pick the best deals 🤷♀️
AI effectively raped and pillaged all human information for the benefit of a handful of corporations. It’s use has been shown to reduce the skills of school children and even deskill those with established knowledge. Then there’s the environmental considerations in a time where the world is decades behind appropriate climate change management… Id say it’s pretty understandable that it evokes passionate responses.
I’m sure OPs RTX 3060 will have a tolerable effect on the school children and the rainforest.
@FauxLiving @YeahToast “I’m sure my driving to work will doom the world” same mentality.
Also, it’s not so much use of a model as the creation of the model. Training is expensive in terms of resources and energy. Using the model and normalizing AI usage leads to more AI usage and more AI normalization, which leads to new models being trained…
Put it into perspective:
- Everyone doing their laundry uses more water than all data centers combined. Golf courses also use vastly more water.
- Data centers use about 1-2% of global electricity.
People driving cars pollutes more as does global shipping, regular old consumerism, etc. The environmental impact of data centers is negligible in the grand scheme of things.
Now that AI is making open source software the most secure software ever written (the hard way, hehe), not using it to scan your stuff is now considered worst practices and basically idiotic negligence. No company that uses software or anything with firmware can afford to ignore it anymore.
There’s no way out of it. Accept the new AI reality and push for good regulations. Example: If we made it so that all AI data centers had to be powered via renewable energy on site that would allay most of the energy use/environmental concerns.
Isn’t ollama made by Meta?
I think you’re thinking of the Llama family of llm models which are owned/made by Meta.
Ollama is an open source server software and model repository for llm models. I won’t say Meta hasn’t contributed to the project, but as far as I know they don’t own it and didn’t start it.
Oh, it could be. I don’t use any online or offline AI tools. I’ve played around with Lumo (from Proton) when it first released bc its/was Mistral for privacy, and work tries to get people on board with AI usage (Copilot).
I just use llama.cpp with Zoo Code, the VSCode extension
join us !localllama@sh.itjust.works
- Pi - plain and simple - run inside firejail.
- lamma.cpp w/ vulkan backend
That’s it.
I’ve got an M4 Mac mini with 32 GB of unified memory, so I can run MoE models in the <40B parameter range quite comfortably. I use LM Studio as the backend with Hermes as the frontend. Works great for everyday chat, web search, and RAG.
This community won’t be much helpful but there is one specific for local LLMs. I think it was ai_ and some other…
It depends on how complex you want to make it.
The simplest way would be to use a GUI tool like LM Studio, it has the benefit of selecting the model quants that are likely to work on your hardware and having sane defaults for the various parameters that you can work from.
Typically you’ll want to increase the context window, the default is good for short one-off questions but if you’re going to do RAG or coding tasks you will need a much larger context window. You will typically find this through trial and error, increasing until you hit memory limits or notice a large slowdown on tokens/second.
You can also have the model respond to an OpenAI-like API interface on a designated port/interface if you want to run non-chat tools through the same model.
Then, depending on the task that you’re trying to solve, you may want to find an MCP server built for that task which can provide the ability for the AI to interact with tools in a way that’s more controlled (giving an AI raw terminal access on a user’s machine is typically a bad idea).
If you need something more flexible that you can incorporate into scripts and automation, llama.cpp has been popular for a while. Like Linux distros, everyone is going to have an opinion on the best and whatever you decide on will be the wrong choice. There’s also Ollama and vLLM if you want to run a datacenter.
Model-wise it completely depends on the task. Do you need a multi-modal model that supports vision, is it going to be coding, is it trained to use tools/MCP servers, does it use reasoning, etc. The Qwen models are open weight and available in many different quants. The version 3.5 models run well on consumer hardware and can use vision, reasoning and tools.
I’m running it through Llama.cpp but it will likely be available via LM Studio as well.
Llama-swap running llama.cpp and strata. For the tools I use oh my pi and nvim with plugins
