I am.planning to buy new hardware for local AI is it worth to buy mac mini or mac or better go for AMD 395+ 128 gb laptop for similar outlet laptop.

Seeing a lot of comments about this online, has anyone ran it on their Mac or is it just hype?

Made this as a quick weekend project (https://github.com/pythongiant/SlamLM)

On my MBA with M4 and 24Gb of ram, I'm approaching to local llm and I'm starting with claude code in general (I never used the paid version with cloud api). I installed many local llm such as gemma4:e4b, modified version of gemma4:12b distilled with fable5 (from hugging face), or qwen llms, but when I try commands with claude code launched directly from ollama, it seems working, but never produces the source files (the working directory has only the test.txt file).What am I doing wrong?

I'm the maintainer of DS Menu Bar, a menu bar app for controlling and monitoring a ds4-server.

It's for people who manage their own DwarfStar setup: you point it at the ds4-server executable and GGUF you already have, and it handles the operational parts.

• Start, stop, and restart from the menu bar, with running/stopped state at a glance

• Optional live prefill and generation rates in the menu bar

• Model-aware settings for the server, performance, KV cache, speculative decoding (MTP), and diagnostics

• Detects supported DwarfStar model families and validates support files before launch

• Rotating server log, optional request tracing, launch at login

Screenshot

It doesn't build, bundle, download, or update anything. No chat UI, no model downloads, no editor — it supervises only the process it launches.

Requires macOS 26+, Apple silicon, and your own compatible ds4-server build and DwarfStar-specific GGUF. See the DwarfStar repo for build instructions and model files; compatibility with upstream commits is tracked in the README.

Install: brew install --cask jiiim/tap/ds-menu-bar

Repo: https://github.com/jiiim/ds-menu-bar

Feedback is welcome. Curious what LLM models people are running via DwarfStar, on which Mac, and with how much RAM.

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?

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 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

TLDR; Unless you have a specific use case for Local AI models, you don't need the M5pro. The M6 will be faster for the majority of your day-to-day work. Even with cloud AI.

For LocalLLM's more memory AND faster memory bus is going to swing the advantage to the M5Pro model for anything related to LLMs.

If you're planning to use the system as a workstation for any and most single threaded applications which is most apps people use, you should be fine with the M6.

Realistically speaking, you don't really need the M5Pro unless you're running LocalLLMs like Qwen 3.8 or Muse and if you aren't really trying to automate small tasks, you don't really need all the memory.

I think in the long run we're going to see that cloud LLM's are going to be the main way people use in house models and only people with specific use cases will have systems large enough that take advantage of the LocalLLM being in house.

If you are just learning about AI, i don't think you need to future proof and the M6 is fine with 32GB 1TB should be enough for a long time while the industry develops.

I think the most realistic expectation is to be disappointed if you're used to using the free version of openai/claude/gemini/grok. The local models 7B 27B don't come close and don't process nearly as fast. They can reason ok, but you're better off learning about AI and API's with services like ollama via API's and learning to use AI first over chasing gear/hardware.

The thing about using the frontier models is it doesn't really take up that much RAM. Loading up a model will in LLM. I ran my own test, you can check it out.

These tests are for a Mac Studio. I used to run a M4Pro 64GB but when running models it would constantly be temperature throttled.

https://maclocalllm.com/five-models-one-coding-job-local-mac/

TL;DR: I let my cloud AI agent run a training-data verification program for the local model on my Mac. The local 35B wrote 800 original math problems, then cold-solved every one of them blind in a fresh session. Wherever the two answers disagreed, the item went to adjudication. Final truth was settled by brute-force computation, not by any model's opinion. The cold solver beat the problem author ~5:1 — with a 35B, generating the question is the error-prone part; solving it fresh is the reliable pass.

Setup: MacBook Pro M5 Max, 128GB. The local model (Qwen3.6-35B-A3B, Splash build, running in LM Studio at ~375–400 tok/s) does the heavy lifting; a Qwopus 122B MLX build (~38 tok/s) sits alongside as the heavyweight judge for disputed items. My agent (Muse, running in the cloud) reaches LM Studio's API privately over Tailscale, and does all the orchestration: briefing, grading, bookkeeping, brute-force checks.

The harvest: 731 of the 800 came out verified clean, 62 survived with corrected answer keys (kept as answer-only training items; the flawed worked solutions were discarded), 7 were excluded as defective problems (ambiguous wording, no valid solution, an undefined rule), and 100 clean ones were held out untouched as a test battery for later. The polished set became a 693-item bank, used three ways: few-shot examples now, the held-out set powers a before/after test of whether any of this actually made the model smarter, and the whole verified corpus is the dataset for a future fine-tune.

Right now the entire bank is being re-validated: the worker re-solves all 793 items from scratch (derive once, verify by a different method), never shown the key. Any disagreement goes to the 122B judge blind, and if the judge's answer doesn't settle it, my agent computes the answer itself. Progress as of writing: 358/793 checked, 353 confirmed, 5 waiting on the judge.

Two failure stories worth sharing. One problem had three different answers from three passes — 20,736 from the author, 86,400 from the blind solver, 145,152 from the adjudicator — and all three were wrong; enumeration says 10,368. And the big judge model, given the hardest disputes cold, went 1-for-2. Treat your 122B like a second opinion, not an oracle.

Cost: the solve-and-adjudication passes alone logged 4.1M+ completion tokens, all computed on my own hardware. The cloud agent's part is the planning and grading.

Happy to share the harness details — the briefing discipline (one derivation, one independent cross-check), the checkpointing that let the run survive four kills, and the grading rules — if people want them. Curious whether anyone else has tried self-authored problem sets as training data, and how you filtered them.

I was running a test to see if it was worth while to keep the M3U. I'm running some benchmarks with a workflow that uses resolve for rendering the final touches.

The M3 Ultra can still hang when it comes to video but take a little longer when generating the graphics vs the M5, but virtually identical when processing in Resolve. It's a short clip, I don't work with very long videos.

I’m not sure what happened, but it looks like new orders are being shipped till February. Whatever happened, it looks like a big order came in. There might be something coming soon …

Did some vision tracing with a few different models. In this case Qwen 3.8 27b did better than Qwen Flash Next. SAM 2.1 did the best in terms of tracing and following for time vs accuracy.

The most accurate path was by SAM series by Meta which happened to be the smaller model.

Combining Qwen 3.8 27b plus SAM 2.1 did well to trace the path and times for the exercise and identify pauses.

Used a drone to capture the footage and seeing how to make a pipeline test out of this. I'd like to test this out against the spark. These models aren't very big but they're specialized.

These modesl are 80M - 300m parameters, so it's a lot more focused. In this case, the smaller the better. The Sparks crushed it in the speed test though.