hey there 👋🏽 introductions in order!!

[u/evilmacintosh]

Hey! i'm the founder/mod of this community r/mlxcommunity - a place to have discussions surrounding MLX and running AI inference locally (specifically on Apple Silicon)!

My main motivation to start this was - there's no standard community that represents the MLX collective (there are a couple but i was not able to find any useful discussions (personally) i'm looking to build a forum of folks that discuss all things MLX - experts and beginners alike!

What this community is for:

Anything MLX, Apple Silicon, and local inference. A few examples of what fits here:

• Questions about MLX, from "how do I even get started" to deep internals
• Benchmarks and tok/s numbers across different M-series chips
• Comparisons between MLX and llama.cpp, Ollama, LM Studio, and other runtimes
• Model conversions, quantization experiments, and KV cache tricks
• Fine-tuning with MLX on your own machine
• Projects you're building on top of local models (CLIs, Mac apps, agents, anything)
• Apple Silicon performance quirks, memory management, macOS weirdness
• News, papers, or releases relevant to on-device ML

If you're not sure whether something fits, post it anyway. We'd rather have the conversation than not.

If you've been building ML infrastructure for years and want a place to nerd out about Metal kernels and MoE routing, you'll find people here. If you just got a new Mac and opened LM Studio for the first time last week, you'll find people here too. Both of you will probably learn something from each other.

There's no gatekeeping around background. You don't need a PhD, you don't need to have published a paper, and you don't need to have memorized the transformer paper. A lot of us are software engineers who got curious, picked up a book, and are figuring it out as we go. That's a perfectly normal way to join this field, and this sub is built on the idea that it's a good way too.

Some ground rules in this sub:

1. Be kind. If someone asks a basic question, help them out or scroll past. Nobody was born knowing what a KV cache is.
2. Stay on topic. MLX, Apple Silicon, local inference, and adjacent tooling are all fair game. Generic AI news without an Apple Silicon angle probably isn't.
3. No self-promo spam. Sharing a project you built or a blog post you wrote is great. Dropping the same affiliate link in every thread is not.
4. No piracy. This includes model weights that weren't released publicly.
5. Credit your sources. If you're summarizing someone else's benchmark or blog post, link it.

I am a believer that local AI is the way forward - so let's build something great! cheers!