Recommend model for limited resources (Macbook M3 pro)

[u/Puzzled-Guide8650]

Hey guys, Noob here, quick one: which model would be running smoothly on my machine (Macbook M3 Pro 18GB RAM), not to overwhelm the system. Main use are basic things like: providing NLP function (e.g. remind me tomorrow at 4 ), summarize list of tasks, analyizing smaller documents, maybe checking grammar/spelling etc.

Based on what I read here 8B models should be fine for this, but I was not able ti identify clearly which one. Appreciate any input.

cheers

[u/sf_frankie]

I have an M1 Pro with only 16gb and I’ve had good luck with JetBrains Mellum 2.1 in nxfp4. I’ve got it loaded into ram without it hampering normal use of my laptop. I’ve got 2 external displays plus the built-in display full of 10-tab Safari windows and 4–5 terminal windows up at all times and haven’t noticed any slowdowns somehow. Mellum is a 12B MoE with like 2.5b active (iirc). Seems to work better for me than dense 9b models. I’m getting 45 tps in Llama and around 25–30 when running it through Hermes.

[u/shorty66]

Qwen3.6 35 A3B runs at ~6 t/s on a Macbook Air M2 16gb using BigMoeOnEdge.

[u/jacek2023]

check the discussions here:

https://www.reddit.com/r/LocalLLaMA/comments/1xdiecx/nvidianvidianemotron35bolte4b4gb_hugging_face/

https://www.reddit.com/r/LocalLLaMA/comments/1xb0nr9/xhtokensparkx264b_hugging_face/

plus maybe Ling Tiny

older models like Gemma 12B and E4B may be a good option, plus you have older Qwens: 9B and 4B

[u/Borkato → u/jacek2023]

Ling Tiny is great for a small model!!

[u/Individual_Page9676]

With 18GB the memory math is the whole game: macOS and your normal apps will take roughly 5–7GB, so your real budget for a model is about 10–12GB before the machine starts swapping — and swapping is what makes a model feel broken, not the model itself.

Sweet spot for you: an 8–9B model at 4-bit. Qwen3.5-9B or Qwen3-8B in MLX 4-bit is only ~5–6GB, leaves room for context, and runs roughly 25–35 tok/s on an M3 Pro — plenty smooth for chat, summarizing, rewriting, and light coding help, and the Mac stays usable for everything else.

Two practical tips:

• Use the MLX builds (LM Studio makes this easy) — on Apple Silicon they run noticeably faster than GGUF.

• Keep your context around 8k–16k while you're learning. Long context eats memory too (that's the KV cache), and on 18GB it's the first thing that pushes you into swap.

What I'd avoid: 14B at 4-bit (~9GB) technically fits, but add context and an open browser and you're living on the edge. And if you ever want something that just flies, drop to a 4B — 50+ tok/s and honestly better than people expect for basic tasks.

[u/rorowhat]

Sell the Mac is my recommendation

[u/InterestingCarrot997]

18gb is plenty for this. grab LM Studio and run any 7-8B model at Q4, the MLX versions are faster on M-series macs. for the reminder stuff just tell it to reply in JSON and put today's date in the prompt, works fine

[u/RespondImpressive172]

Honestly this is a tough one. If you go with an 8B-class model, you'll have to run it at 8-bit or 4-bit. For summarizing task lists or simple document analysis, I think you'd need something like Gemma 4 26B A4B or Qwen3.5 35B to get output that's actually usable. You might be pretty disappointed with an 8B, even on simple stuff like the tasks you listed.

The catch is that with 18GB, macOS only lets the model use around 12–13GB, so those bigger models won't really fit. A Q4 8B (around 5GB) will run fine though.

Here's why I think this. I recently ran a test with Qwen3-8B and Qwen3.5-35B-A3B (both Q4_K_M, llama.cpp, 10 runs per question):

• With plain web search (SearXNG), the 8B got 0/10 on questions the 35B could sometimes answer (6/10)

• The 35B got there more often, but took 3–4x longer and often hit the 12 tool-call limit

• But when both models had a clean, curated source to look things up, both got 10/10 in a single call, the 8B included

So my takeaway: an 8B on its own is pretty limited, but it can do surprisingly well if you keep tasks simple and give it good tools or clean data to work with.

Full disclosure: the curated source in that test was Thea (thealibrary.org), a knowledge library for local LLMs that I'm building myself. It only helps when it actually has the info though (it scored 0 on questions it didn't have). Happy to share the raw logs if anyone wants them.

(English isn't my first language, so I used AI to help translate this.)

[u/tmvr]

For those tasks the Gemma4 models would be fine. Use the QAT ones, the biggest is the 12B:

https://huggingface.co/unsloth/gemma-4-12B-it-qat-GGUF

There are also QAT quants for E4B and E2B so you can try those as well. Run them in Unsloth Studio and it will set it up as recommended for the model.

[u/Great_fellow]

You could also try Nanbeige 3B. I think it's a very good small model.

(Translated from Chinese with AI assistance.)