Who uses open LLMs and coding assistants locally? Share setup and laptop

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• Hacker News
• Hardware: MacBook Pro M4 Max, 128GB

  Platform: LMStudio (primarily) & Ollama

  Models:

  - qwen/qwen3-coder-30b A3B Instruct 8-bit MLX

  - mlx-community/gpt-oss-120b-MXFP4-Q8

  For code generation especially for larger projects, these models aren't as good as the cutting edge foundation models. For summarizing local git repos/libraries, generating documentation and simple offline command-line tool-use they do a good job.

  I find these communities quite vibrant and helpful too:

  - https://www.reddit.com/r/LocalLLM/

  - https://www.reddit.com/r/LocalLLaMA/

  by erikig
• If you're going to get a MacBook, get the Pro, it has a built-in fan, you don't want the heat just sitting there on the MacBook Air. Same with the Mac mini, get the studio instead, it has a fan, the Mini does not. I don't know about you but I wouldn't want my brand new laptop / desktop to be heating up the entire time I'm coding with 0 cool off. If you go the Mac route, I recommend getting TG Pro, the default fan settings on the Mac are awful they don't kick in soon enough, TG Pro lets you make it a little more "sensitive" to those temperature shifts, its like $20 for TG Pro if I remember correctly, but worth it.

  I have a MacBook Pro with an M4 Pro chip, and 24GB of RAM, I believe only 16 of it is usable by the models, so I can run the GPT OSS 20B model (iirc) but the smaller one. It can do a bit, but the context window fills up quickly, so I do find myself switching context windows often enough. I do wonder if a maxed out MacBook Pro would be able to run larger context windows, then I would easily be able to code all day with it offline.

  I do think Macs are phenomenal at running local LLMs if you get the right one.

  by giancarlostoro

•   Models
      gpt-oss-120b, Meta Llama 3.2, or Gemma
      (just depends on what I’m doing)
  
    Hardware
      - Apple M4 Max (128 GB RAM)
        paired with a GPD Win 4 running Ubuntu 24.04 over USB-C networking
  
    Software
      - Claude Code
      - RA.Aid
      - llama.cpp
  
    For CUDA computing, I use an older NVIDIA RTX 2080 in an old System76 workstation.
  
    Process
  
      I create a good INSTRUCTIONS.md for Claude/Raid that specifies a task & production process with a task list it maintains. I use Claude Agents with an Agent Organizer that helps determine which agents to use. It creates the architecture, prd and security design, writes the code, and then lints, tests and does a code review.

  by jetsnoc
• For anyone who wants to see some real workstations that do this, you may want to check out Alex Ziskind's channel on YouTube:

  https://www.youtube.com/@AZisk

  At this point, pretty much all he does is review workstations for running LLM's and other machine-learning adjacent tasks.

  I'm not his target demographic, but because I'm a dev, his videos are constantly recommended to me on YouTube. He's a good presenter and his advice makes a lot of sense.

  by softfalcon
• On a Macbook pro 64GB I use Qwen3-Coder-30B-A3B Q4 quant with llama.cpp.

  For VSCode I use continue.dev as it allows to set my own (short) system prompt. I get around 50token/sec generation and prompt processing 550t/s.

  When giving well defined small tasks, it is as good as any frontier model.

  I like the speed and low latency and the availability while on the plane/train or off-grid.

  Also decent FIM with the llama.cpp VSCode plugin.

  If I need more intelligence my personal favourites are Claude and Deepseek via API.

  by dust42
• For new folks, you can get a local code agent running on your Mac like this:

  1. $ npm install -g @openai/codex

  2. $ brew install ollama; ollama serve

  3. $ ollama pull gpt-oss:20b

  4. $ codex --oss -m gpt-oss:20b

  This runs locally without Internet. Idk if there’s telemetry for codex, but you should be able to turn that off if so.

  You need an M1 Mac or better with at least 24GB of GPU memory. The model is pretty big, about 16GB of disk space in ~/.ollama

  Be careful - the 120b model is 1.5× better than this 20b variant, but takes 5× higher requirements.

  by gcr
• Ollama, 16-CPU Xenon E6320 (old), 1.9Ghz, 120GB DDRAM4, 240TB RAID5 SSDs, on Dell Precision T710 ("The Beast"). NO GPU. 20b (n oooooot f aah st at all). Pure CPU bound. Tweaked for 256KB chunking into RAG.

  Ingested election laws of 50 states, territories and Federal.

  Goal. Mapping out each feature of the election and deal with (in)consistent terminologies sprouted by different university-trained public administration. This is the crux of hallunications: getting a diagram of ballot handling and their terminologies.

  Then maybe tackle the multitude ways of election irregularities, or at least point out integrity gaps at various locales.

  https://figshare.com/articles/presentation/Election_Frauds_v...

  by egberts1
• I sometimes still code with a local LLM but can't imagine doing it on a laptop. I have a server that has GPUs and runs llama.cpp behind llama-swap (letting me switch between models quickly). The best local coding setup I've been able to do so far is using Aider with gpt-oss-120b.

  I guess you could get a Ryzen AI Max+ with 128GB RAM to try and do that locally but non-nVidia hardware is incredibly slow for coding usage since the prompts become very large and take exponentially longer but gpt-oss is a sparse model so maybe it won't be that bad.

  Also just to point it out, if you use OpenRouter with things like Aider or roocode or whatever you can also flag your account to only use providers with a zero-data retention policy if you are truly concerned about anyone training on your source code. GPT5 and Claude are infinitely better, faster and cheaper than anything I can do locally and I have a monster setup.

  by lreeves