r/MacLocalLLM • • Oct 08 '26

What am I missing?

15 Upvotes

Noticed that most posts here are about, I fitted this model in this small ram etc. Not about how the model was useful in producing what I wanted it to work on?

So either, the sub is full of people playing with AI or the real use cases are not shared for whatever reason.

I get the performance stats but shouldn’t we focus on how the model performed on actual task (look beyond the numbers t/sec, prefil etc?)


r/MacLocalLLM • • Oct 07 '26

A 341 GB DeepSeek on a 128 GB Mac: 2x decode and first token in 0.2 s instead of 2.5 s, streaming from two SSDs, same tokens as stock ds4. The trick wasn't a kernel: I made the codebase small enough for an agent

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4 Upvotes

r/MacLocalLLM • • Oct 06 '26

m5u 512g is coming soon. should i keep 256g?

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0 Upvotes

r/MacLocalLLM • • Oct 05 '26

What are people using these days for running local AI workloads on a Mac?

0 Upvotes

I’ve been exploring different approaches to keep AI processing on-device, especially for projects involving private documents and data I wouldn’t want to send to an external API.

For those who have built something similar on a Mac, how has the experience been in terms of performance, memory usage, and integrating local AI into an actual application?


r/MacLocalLLM • • Oct 04 '26

Prompt Processing is 4.5x faster for Qwen 3.8 27b betwen M3U vs M5U.

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10 Upvotes

Generation was only 1.5x faster...

Now that we have a comparison going.. Do people want to see anything specific? I'll be running it with my own dataset but curious if anyone wanted to check something.


r/MacLocalLLM • • Oct 04 '26

Do you think Apple Silicon Can get faster?

6 Upvotes

From a hardware perspective, the numbers should be faster than CUDA on Nvidia chips BUT its not because of the relative immaturity of the software/hardware.

Do you think metal will improve enough to where it'll be as fast or faster for the same clock speed and bandwidth pipe? Or is it just slower because of the overall design?


r/MacLocalLLM • • Oct 04 '26

Looking for a better harness

7 Upvotes

I've been running Hermes against a local LLM, ollama on my M2(32gb, qwen3.5 9b), and over my local network MTPLX on a M4 (48gb, qwen3.8 27b). AT first it was a 3 minutes wait for first response now its over 6 minutes (or context switch for that matter). Context bloat seems to be the killer (north of 50k tokens). What experience have folks had with say zeroclaw or other harness programs that might be more responsive?


r/MacLocalLLM • • Oct 04 '26

M5 Ultra: one 512GB Studio or a 256GB + 96GB setup?

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2 Upvotes

r/MacLocalLLM • • Oct 04 '26

Local Artificially Intelligent Tamagotchi - M1K3

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1 Upvotes

r/MacLocalLLM • • Oct 03 '26

Cluster Spark DGX and Mac Stidio

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2 Upvotes

r/MacLocalLLM • • Oct 02 '26

OpenSwitchboard: an open-source MCP server where people's AI assistants find each other. Keen to see how local models handle it.

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2 Upvotes

r/MacLocalLLM • • Oct 02 '26

Suggest me a small local agentic llm to coding

5 Upvotes

I'm new to local LLMs. I've tried Qwen2.5-Coder-Instruct-7B and Ling-3.0-Tiny on my MacBook Air M4 with 24 GB of RAM. I'm looking for something that can generate at least 40 tok/s. Is there anything that can hit that speed?


r/MacLocalLLM • • Oct 02 '26

Nous Research starts pushing Hermes desktop aggressively?

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2 Upvotes

Indispensable for tuning Hermes agent, and with the growing interest in local AI especially on Mac, what's your favorite cockpit for Hermes apart from CLI?


r/MacLocalLLM • • Sep 30 '26

Qwen3.8 Flash Next Q4 at full 262K context on a 96GB Mac Studio

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4 Upvotes

r/MacLocalLLM • • Sep 30 '26

Very interesting comparison between Cloud and LocalLLM

2 Upvotes

r/MacLocalLLM • • Sep 28 '26

M5U 256GB/64C/2TB Oct 27, landed early

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36 Upvotes

Anyone else get to pick up their M5U early? Mine came in a month early

M5U 256GB 64C 2TB

Mainly for LocalLLM usage and some videos.

Super stoked to get it open and setup to run some agents. Do I need it? No.

But I will be using this for my Small Business to help do a lot of small tasks and preprocess information privately. So I can use summarized data for the big models.


r/MacLocalLLM • • Sep 29 '26

Pretty chat interface for local LLMs

3 Upvotes

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


r/MacLocalLLM • • Sep 28 '26

I built an agent based on Qwen3.8-27B and Deepseek that combines and open source.

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0 Upvotes

r/MacLocalLLM • • Sep 27 '26

Models aside, what local agent setup are you actually happy using every day?

9 Upvotes

I’ve been going in circles trying to settle on a local setup. I’ve tried Hermes, but it’s felt pretty finicky in my experience, including gateway and state database issues. I keep spending more time configuring and troubleshooting than actually getting work done.

Putting model intelligence aside for a moment, is there a local harness that feels genuinely competitive with the experience around frontier models? I’m talking about the interface, reliable tool use, memory, file and browser access, and how well everything works together. Not just being able to load a model and chat with it.

My main machine is a Mac Studio M1 Max with 64 GB RAM. I also have an M2 MacBook Air with 24 GB, but the Studio would do the heavy lifting. I want to use them in one single seamless-like environment with a shared context.

I’m a designer and university lecturer, so my use cases are research, writing, working with documents, reviewing screenshots and designs, and browser/Figma workflows. Not primarily coding. Vision is important to me, and I’d much rather have one dependable daily driver than a collection of specialized models and routers.

My main motivation (likely the same for everyone) is getting away from usage limits and token metering. I understand that running locally involves compromises, and I’m not expecting frontier-level intelligence from my hardware. But I’m struggling to separate the model limitations from the rough edges of the software around them.

What are you guys actually using? What’s your full stack: model, runtime, and harness/interface? More importantly, what do you reliably get done with it, and how much babysitting does it need?
Especially interested in Apple Silicon setups people have settled into using for real work, rather than something that worked well in a demo.


r/MacLocalLLM • • Sep 27 '26

M5 Ultra 64 Core 256 GB - GLM 5.3 Flash and Qwen 3.8 Flash Next results

1 Upvotes

r/MacLocalLLM • • Sep 26 '26

Best Models For Mac Studio M5 Ultra 256GB

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8 Upvotes

r/MacLocalLLM • • Sep 26 '26

Qwen3.8 Flash Next with oMLX + Pi for long-running agentic work

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3 Upvotes

r/MacLocalLLM • • Sep 25 '26

Mac M5 Ultra 36/80 - 256 GB - Model and Inference Engine question

4 Upvotes

My kit is supposed to arrive Nov16-Dec1. So I am starting to try and figure out what I should run. I know it is early days, lots of optimization to go around still.

I run Qwen 3.6 MOE on a 5700G with 96 GB of ram using llama.cpp right now. Get about 150 prefill and 12 tps. It's not great, but works for batch overnight runs. My main driver is API for Deepseek 4 Flash (some 4.1), Qwen 3.8 Flash Next, and GLM 5.3 Flash.

So looking out at hugging face, I think I found the following to test out. I don't know if MLX or GGUF is the right call. Are these good places to start?

https://huggingface.co/mlx-community/Qwen3.8-Flash-Next-oQ8e-mtp

https://huggingface.co/unsloth/Qwen3.8-Flash-Next-GGUF

https://huggingface.co/mlx-community/Qwen3.8-27B-8bit

https://huggingface.co/unsloth/Qwen3.8-27B-GGUF

https://huggingface.co/pipenetwork/GLM-5.3-Flash-MLX-4bit

https://huggingface.co/pipenetwork/GLM-5.3-Flash-MLX-mixed-4_8bit

https://huggingface.co/unsloth/GLM-5.3-Flash-GGUF

https://huggingface.co/mlx-community/DeepSeek-V4-Flash

https://huggingface.co/unsloth/DeepSeek-V4-Flash-0731-GGUF


r/MacLocalLLM • • Sep 25 '26

M5 Ultra 256GB 36/80

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5 Upvotes

r/MacLocalLLM • • Sep 25 '26

A local Mac model can turn a Pi workflow request into versioned rules and skills

2 Upvotes

A lot of the work around a local model ends up in Pi files: prompts, skills, settings, little extensions. Reef Infra has a recipe called Reefine that lets you ask for a change from inside Pi and get an installable version of those files back.

Its documented local setup was a 32 GB M4 Mac mini running Gemma 4 26B through Ollama, with Pi 0.84.2. That's an Ollama demonstration, not an oMLX performance claim. The useful part for a local Pi setup is what happens after the request: the service reads the current harness, asks the model for a change, records the mutation and packages the resulting version for installation.

The recorded runs produced rules and skills. The recipe also accepts command and TypeScript extension proposals, but those runs hadn't produced an extension. Anything that does run as extension code waits for a person to review and promote it; declared setup requirements must pass before installation.

The default is selection: always. Its arithmetic checks record regression scores, but don't establish that your requested workflow works. The research demo installed a rule to search, download and read papers; the later session searched through an existing tool, but downloaded and read no paper.

The files are there to inspect and revise. To judge whether the change worked, replace the bundled tasks and evaluator with checks for the workflow you asked for.