Personally, I'm getting 15 tok/s on both RTX 3060 and my MacBook Air M4 (w/ 32GB, but 24 should suffice), with the default config from LMStudio.

Which I find even more impressive, considering the 3060 is the most used GPU (on Steam) and that M4 Air and future SoCs are/will be commonplace too.

(Q4_K_M with filesize=18GB)

One of the most interesting things about that model is its excellent score on the RAG confabulations (hallucination) leaderboard. It’s the 3rd best model overall, beating all OpenAI models, for example. I wonder what Alibaba did to achieve that.

https://github.com/lechmazur/confabulations

What tasks have you found the 0.6B model useful for? The hallucination that's apparent during its thinking process put up a big red flag for me.

Conversely, the 4B model actually seemed to work really well and gave results comparable to Gemini 2.0 Flash (at least in my simple tests).

Importantly they note that using a draft model screws it up and this was my experience. I was initially impressed, then started seeing problems, but after disabling my draft model it started working much better. Very cool stuff- it's fast too as you note.

The /think and /no_think commands are very convenient.

how much RAM do you have? I want to compare with my local setup (M4 Pro)

I am curious if you have tried function calling or MCPs with it?

It's fits entirely in my 7900xtx memory. But tbh i've been disappointed with programming ability so far.

It's using 20GB of memory according to ollama.

I’m getting 56 with mlx and lmstudio. How 76?

I went form bottom up, started with 4B, then 8B, then 30B, and 30B was the only one that started to "use tools". Other models were saying they will use but never did, or didnt notice all the tools. I think anything above 30b would atually be able to go full GPT on a task. 30b does it, but a bit.. meh

What *is* vibe coding? And how can I stop hearing about it for the rest of my life?

Definitely try it, it can navigate files search for stuff, run bash commands, and while 30b is a bit cranky it gets the job done (much worse then i would get when i plug in gpt-4.1, but its still not bad, Kudos o qwen. As for localforge, it really is a vibe coding tool, just like claude or codex, but with the possibility to plug more than just one provider. What's wrong with that?

You should try it. It's trained for tool calling and thinks before taking action.

Now how do you feed it an existing codebase as part of your prompt? Does it even support that (prompt size etc).

There's a few Ollama-MCP bridge servers already (from a quick search, also interested myself):

ollama-mcp-bridge: A TypeScript implementation that "connects local LLMs (via Ollama) to Model Context Protocol (MCP) servers. This bridge allows open-source models to use the same tools and capabilities as Claude, enabling powerful local AI assistants"

simple-mcp-ollama-bridge: A more lightweight bridge connecting "Model Context Protocol (MCP) servers to OpenAI-compatible LLMs like Ollama"

rawveg/ollama-mcp: "An MCP server for Ollama that enables seamless integration between Ollama's local LLM models and MCP-compatible applications like Claude Desktop"

How you route would be an interesting challenge, presumably could just tell it to use the mcp for certain tasks, thereby offloading locally.

I've been toying with Visual Studio Code's MCP and agent support and gotten it to offload things like reference searches and targeted web crawling (look up module X on git repo Y via this URL pattern that the MCP server goes, fetches and parses).

I started by giving it a reference Python MCP server and asking it to modify the code to do that. Now I have 3-4 tools that give me reproducible results.

This is the closest I've found that's akin to Claude Code: https://aider.chat/

Here's qwen-30b-a3b's response to your prompt when I worded it better:

The prompt was:

"Create a Python decorator that registers functions as handlers for MQTT topic patterns (including + and # wildcards). Internally, use a trie to store the topic patterns and match incoming topic strings to the correct handlers. Provide an example showing how to register multiple handlers and dispatch a message to the correct one based on an incoming topic."

https://pastebin.com/wefw7X2h

I think your prompt is bad. Still impressive that Claude 3.7 handled your bad prompt, but qwen3 had no problem with this prompt:

Create a Python decorator that registers functions as handlers for MQTT topic patterns (including + and # wildcards). Internally, use a trie to store the topic patterns and match incoming topic strings to the correct handlers. Provide an example showing how to register multiple handlers and dispatch a message to the correct one based on an incoming topic.

Is there an additional system prompt before that? Or i can repro with just this?

You should probably try a different quantization, have you try UD-Q4_K_XL?

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you can combine agents in 2 ways, you can constantly swap agents during one conversation, or you can have the in separate conversations and collaborate. I was even thinking 2 agents can work on 2 separate git clones, and then do PR's to each other. I also like using code to do the image parsing and css, adn then gemini to do the coding. I tried using gemma and qwen for "real stuff" but its more of a, simple stuff only, if i really need output, id' rather spend money for now. Hopefully to change soon. as for rotuing, localforge does NOT know. you choose the agent, and it will loop inside that agent forever. Like, the way it works is that unless agent decides to talk to a user, it will forever be doing function calls and "talking to functions", as one agent. The only routing happens this way. there is main model and there is Expert model. main model knows to ask expert model (see system promp), when its stuck. so for any rouing to happen 1) system prompt needs to mention it 2) a routing to another model should be a function call. that way model knows how to ask another model for something

Personally, I assumed that a blog post on the domain localforge.dev was written by the developers of localforge, but I might be wrong.

I just tried it. It got stuck looping on a `cargo check` call and literally wouldn't do anything else. No additional context, just repeatedly spitting out the same tool call.

The problem is the best models barely clear the bar for some stuff in terms of coherence and reliability; anything else just isn't particularly usable.

Litellm is definitely your best bet. For recording - you can probably vibe code a proxy in front of it that mitms it and dumps the request into whatever format you need

I’ve been looking for this for my team but haven’t found it. Providers like OpenAI and Anthropic offer admin token to manage team accounts and you look hook into Ollama or another self managed service for local AI.

Seems like a great way to roll out AI to a medium sized team where a very small team can coordinate access to the best available tools so the entire team doesn’t need to keep pace at the current break-neck speed.

I'm a maintainer of Opik, an open source LLM eval/observability framework. If you use something like LiteLLM or OpenRouter to handle the proxying of requests, Opik basically provides an out-of-the-box recording layer via its integrations with both:

https://github.com/comet-ml/opik

Could you maybe make use of Simon Willsons [LLM lib/app](https://github.com/simonw/llm)? It has great LLM support (just pass in the model to use) and records everything by default.

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To add, you can use so called, abliterated models that are stripped of censorship for example. Much better experience sometimes.

I think we are just tiny bit away of being able to really "code" with ai, locally. Because even if it would be on gemini2.5 level, since its free, you can make it self prompt a bit more and eventually solve any problem. if i could ran 200b or if 30b wouldve been as good - it wouldve been enough

If you have a 32GB Mac then you should be able to run up to 27B params, I have done so with Google's `gemma3:27b-it-qat`

Karpathy's original tweet defining "vibe coding":

https://x.com/karpathy/status/1886192184808149383

If you don't mind going through the eldritchian horror that is building ROCm from source[0], Qwen_Qwen3-30B-A3B-Q6_K (6bit quantization of the LLM mentioned in the article which in practice shouldn't be much different) works decently fast on a RX 7900 XTX using koboldcpp and llama.cpp. And by "decently fast" i mean "it writes faster i can read".

If you're on Debian AFAIK AMD is paying someone to experience the pain in your place, so that is an option if you're building something from scratch, but my openSUSE Tumbleweed installation predates the existence of llama.cpp by a few years and i'm not subjecting myself to the horror that is Python projects (mis)managed by AI developers[1] :-P.

EDIT: my mistake, ROCm isn't needed (or actually, supported) by koboldcpp, it uses Vulkan. ROCm is available via a fork. Still, with Vulkan it is fast too.

[0] ...and more than once as after some OS upgrade it might break, like mine

[1] ok, i did it once, because recently i wanted to try out some tool someone wrote that relied on some AI stuff and i was too stubborn to give up - i had to install Python from source on a Debian docker container because some dependency 2-3 layers deep didn't compile with a newer minor version release of Python. It convinced me to thank yet again to thank Georgi Gerganov for making AI-related tooling that enables people to stick with C++

Entirely due to the unified RAM between CPU and GPU in Apple Silicon. Laptops otherwise almost never have a GPU with sufficient RAM for LLMs.

The new AMD chips in the Framework laptops would be a good candidate and I think you can get 96GB RAM in them. Also if the LLM software is idle (like llama.cpp or ollama) there is negligible extra power consumption.

This is mostly for one of 4 reasons:

1. Sovereignty over data, your outputs can't be stolen or trained on

2. Just for fun / learning / experiment on

3. Avoid detection that you're using AI

4. No Internet connection, in the woods at your cabin or something

With 16gb you can comfortably run a 12b model that's been quantized. Quantizing is (bad example) effectively lossy compression.

It's been a solid trend for the last two years: I've not upgraded my laptop in the time and the quality of results I'm getting from local models on that same machine has continued to rise.

My hunch is that there's still some remaining optimization fruit to be harvested but I expect we may be nearing a plateau. I may have to upgrade from 64GB of RAM this year.

I was just trying to make sure is maximally performant, and did it with MLX because i am running on mac hardware and wanted to be able to run 30b in reasonable time so it can actually autonomously code something. Otherwise there are many ways to do it!

I believe mlx will allow you to run the models marginally faster (per a recent blog post by @simonw)

mlx is an alternative model format to GGUF. It executes natively on apple silicon using Apple's AI accelerator, rather than through GGUF as a compute shader(!). It's faster and uses fewer resources on Apple devices.

Yes, Ollama has Qwen 3 and it works great on a Mac. It may be slightly slower than MLX since Ollama hasn't integrated that (Apple Silicon optimized) library yet, but Ollama models still use the Mac's GPU.

https://ollama.com/library/qwen3

I’m also team 24GB.

Is anyone using a similar setup with 24GB RAM? Which model would you recommend?

I only saw a sub-1B model mentioned in other comments, but that one seems too small.

I expect that will run Qwen 3 8B quite happily, and I've found that to be a surprisingly capable model for its size.

The 30B one requires 20 GB of memory for me. But some of the lower parameters one should be ok

i think MPS is the term for the APIs apple exposes to control their GPUs and MLX is a machine learning framework optimized for using MPS.

In case someone didn’t see that yet, vibe coding has a recent and more specific meaning.

https://en.m.wikipedia.org/wiki/Vibe_coding

> a programming paradigm dependent on artificial intelligence (AI), where a person describes a problem in a few sentences as a prompt to a large language model (LLM) tuned for coding.

> A key part of the definition of vibe coding is that the user accepts code without full understanding.

And the "for free" in the title excludes the electricity costs

That's just called coding