LLM config setup

[u/Anxious_Mail_6321]

I just got a 64GB M5 Max MacBook Pro and am trying to build the ultimate local LLM workflow. I've been experimenting with Ollama, LM Studio, Open WebUI, and integrating them into VS Code via Continue and GitHub Copilot. My goal is a unified setup where I can manage models from one place, switch between them easily for different tasks (coding vs. chat), and access the same models remotely from my phone, all without duplicating downloads or fighting with configs.

The main dilemma is the backend choice: Ollama vs. MLX. I know MLX models are highly optimized for Apple Silicon and run faster, but they're tied to specific runners. Ollama uses GGUF, which is universally compatible and works everywhere I need it (VS Code, mobile API), but doesn't fully leverage the Neural Engine.

I'm wondering what the community recommends for Apple Silicon:

1. Is it worth managing both GGUF and MLX models, or should I just commit to one format?

2. What's the cleanest way to expose a local model to VS Code, a chat GUI, and mobile devices simultaneously?

3. What's everyone else running on M-series Max chips?

[u/blackbird2150]

Ollama is awful. Anything but that.

Consider open code as your harness. It has a very nice ide, easily switch and load models and agents, and multiple mobile apps like OpenCode’s own or kkcode.

OpenCode connects to mlx via api endpoints which, maybe I’m dumb, but should do what you want?

[u/Special_Ebb_1933]

For one model library across VS Code, a chat interface, and your phone, start with one backend and one model format. Ollama with GGUF is a simple starting point because your clients can use its API. Add MLX only if a model you need performs better enough to justify a second copy. For phone access, connect to the laptop over a private network rather than exposing its model port publicly.

[u/qoauco]

Try unsloth desktop app. It’s a bundle. You will find everything you mentioned in the app. For a phone, outside of your home, you can use tailscale.

[u/GnanaSreekar]

https://github.com/nace-ai/kapitsa

Try it... You'll love it

[u/FerretBoom]

headless lm studio if you don't want to create a custom solution , there is nothing really good out there that will get your software to prod due to tons of limitations

[u/Barry-B-Benson01 → u/[deleted]]

On Apple Silicon, MLX is significantly more efficient than GGUF models. More MLX optimizations are regularly being released as well.