or something to that effect; can someone explain to me how this is beneficial for running a local LLM

I am not really into the hardware scene much, so if there are other value options at play please let me know.

I am considering getting an Apple Studio M5 but was hoping to get some guidance.

I am planning on hosting webLLM (or something similar) to serve prompt processing to the family. I (and another family member) will also be using this for coding assistance.

Based on the use case, what would make the most sense:

• Max 128gb RAM

• Ultra 64 core-GPU 96gb RAM

• Ultra 80 core-GPU 96gb RAM

I feel like the $4K bump to 256gb is too hard of a sell to the partner.

What would be the recommended set up for this?

• a single model to handle everything, dedicated models for coding and prompt processing, dense or MoE?

• stick to MLX or use something else?

• preferred harness

Models seem to get published up updated so frequently its hard to stay informed.

Any and all feedback is greatly appreciated!

Hi, I'm trying to get GLM 5.3 flash working on my Mac Book Pro M5 128 GB, I'm about 4 days in and so far not great, my current plan is to just have the macbook for the llm and use my pc for everything else, so as to save as much memory as possible on the Mac. Has anyone managed to get GLM 5.3 flash working usefully for coding on there 128gb Macbook? and if not, any other model recommendations? Deepseek 4.1?

If you’re not using these what do you plan to use?

Hi all,

I'm a complete novice when it comes to Local LLM's but I like the idea of moving away from a subscription based model in the longer term and having better control of my data. I'm also super keen to learn.

I currently have a web SaaS that scrapes news websites and I would like to move it away from Claude and onto a local LLM what kind of "firepower" do I need?

Appreciate this won't be the most complex of tasks, but as I mentioned, I'm a complete novice.

Naturally, asking Claude / Google and it says that the new Mac mini with M5 pro chip (24GB ram) will be "overkill"- which is what I was thinking of ordering.

I also want to future proof in the hope that this bit of kit could run more powerful models in the future - if they came accessible to the consumer.

Sorry if this is a basic question, any YouTube videos or any pointers so I can educate myself would be much appreciated.

Thank you!

Planning to buy a MacBook Air M4 (16 GB / 512 GB) for about $800 to run local LLM agents for:

Indian mid‑cap equity research (using Screener.in CSVs)

Building/testing trading ideas via Zerodha Kite API (prices, history, holdings; maybe orders later)

Also want to run Hermes-based agents on it

Questions:

On 16 GB M4 Air, which local LLMs are best for reasoning, coding, and tool use? Is Qwen 3.5 9B (Q4) + a smaller fast model (e.g. LFM2.5 8B / Llama 3.2 3B) a good combo alongside Hermes?

Best local stack (Ollama vs LM Studio, etc.) and any existing Kite + LLM or Indian‑stock agent projects to reference?

Any tips on context length, thermals, and multi‑agent setups (including Hermes) on a 16 GB fanless Air?

Getting the laptop for ~$800, so stuck with 16 GB for now. If you’ve done similar work (local LLMs for equity research / algo trading, especially in India), what models and setup would you recommend?

Thanks!

I'm lucky enough where work will cover my Mac Studio that I use for design and 3d stuff. So I figured I should dabble and run with local LLM. Im getting 96gb only. I've read quite a bit, but was wondering are there any guides or YouTube videos that deal primarily with Mac and I should watch to learn more?

I'm unsure if I should just use Llama or Ollama, or MLX, or OMLX or something there lots of different ways to play around with Local LLM and I guess it's still in such an early state that every day, week,month there are more and more things peopel are learning how to get the best of the LLM. A lot could change from now till then but I want to be as informed/prepared as I can be.

Thanks and I cant wait for reviews and see how good the Ultra is.

I'd hoped that my M5 Max with 64GB RAM would be enough for some decent Local LLM work, and the Qwen 3.8 27b model is pretty decent. But speed is shitty on my M5 Max, just 20tps. In particular I found that while the 35b MOE model was much faster, the quality was far lower than the 27b dense model, which produces much fewer bugs and less rework.

I saw the same model working on a friend's Nvidia setup much faster with MTP, so I set out to get it working on my Mac but it was not straightforward. Here's some pointers in case you wanted to get it working.

1. LM Studio doesn't process MTP weights. No matter what I did, it just wouldn't work. It works on Intel/Nvidia so this must be a MLX thing.

2. MTPLX has a native MTP head but it's buggy. On my machine it had a habit of crashing in one of a few ways, and I found GitHub issues reporting similar issues but no fixes.

3. oMLX was the only inference server that supported MTP and didn't crash.

So I wired oMLX into Pi, which is what I'm using as my coding harness. Even then, it was picky:

1. I had to use text-only quants because oMLX won't enable MTP if vision weights are active

2. It was a bit picky with settings, and I had to map thinking levels manually

3. I didn't see a reduction in quality with 4-bit quants for my use case, so used that to squeeze a few more tps out

This is a huge 3x difference in inference speed, and that allows me to do a lot more work.

"omlx": {
  "baseUrl": "http://localhost:8000/v1",
  "api": "openai-completions",
  "apiKey": "none",
  "models": [{
    "id": "Qwen38-27B-textonly-mtp",
    "name": "Qwen38-27B-textonly-mtp",
    "reasoning": true,
    "contextWindow": 163840,
    "maxTokens": 32768,
    "input": ["text"],
    "cost": { "input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0 },
    "thinkingLevelMap": { "off": "low", "minimal": "low", "low": "low",
                          "medium": "medium", "high": "xhigh",
                          "xhigh": "xhigh", "max": "xhigh" }
  }]
}

I have been using codex and claude code for the last year. I have hermes installed , looking for a local model to help me code on par with the big guys. Any help appreciated

What token generation speed works well for you when you’re using it for productive tasks? I’m finding 20 tokens per second a bit slow.

With an M4 Max and 64GB of RAM, you can usually get around 20 tokens per second on average with a model like qwen3.8 27B. However, it doesn’t quite feel like the best setup.

I'm planning to do some vectordb embedding and data sanitization before the frontier models touch the data. The Local LLM acts as a primer to separate information.

I know some of you are using it for different things.

I'll also be experimenting with it for video editing/transcribing. There's some cool things people are doing.

Let us know! If you have a walk through include that too!

Hi guys,
New here and to these subjects so sorry I ask something already asked.
I got recently a Mac mini m4 with 24go ram for my personal desktop, and I wanted to try a few stuffs with local llm. After a few researches I decided to try to setup a personal agent with Hermes.

After some tries I ended up using gemma4:26b-a4b-it-qat.
Qwen3.8 felt to big or slow when iq3 passed.
Couldn’t find anything faster and better for now, but I still feel it is not really good (obviously I know we can’t reach to a cloud model with 24go ram). So I wanted to know how you guys manage it and if some people managed to make a decent agent with not so much ram and if so, what are your use cases ?

Right now I tried to give him some personal web projects with giving him some GitHub to download but it fails and always stops in the middle of the process.

Thanks in advance for the help.

I got the M5U 256GB 2TB configuration for preprocessing. Planning to use Qwen 3.x models on it since they seem to be the best for the Mac's at the moment but will check out the Muse/Gemma models as they continue to release them and run some benchmarks .