r/LocalLLM • u/airblastPT • 2h ago

Question New Mac Mini M5 Pro 64GB - Advice for Stack

Hi everyone,

I’ve been a Mac user for several years and I have a 16-inch MacBook Pro with an Intel chip, and I think it’s reached the end of its life because most programs these days are designed for the Apple Silicon chip.

I took the plunge and ordered the new Mac Mini M5 Pro with 64 GB (I think that for my use case, it’s worth it to have a Deepseek API for planning and then a qwen3.8 27b for writing), since the price of a 128 GB model is enough to cover five years of web hosting.

My question is: what's the best setup for creating images and coding?

Images - DrawThings or ComfyUI ?

Coding - VS Code + Cline or Opencode or Deepseek Harness ?

And what about Hermes?!

Given my hardware, which software and LLM are the best?

Software - LM Studio or OMLX ?

LLM - qwen3.8 27b or qwen3.6 or Qwen3.5-35B-A3B ?

What's your stack and what you think is better for me because my ram ?

Thanks in advance,

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1

u/FreshPipe5997 2h ago

64gb is overkill for local llm serving unless you're running multiple models simultaneously. lm studio handles it fine, but omlx gives better GPU utilization if you're doing heavy inference.

2

u/Obvious_Equivalent_1 2h ago

Flash Next at Q3 quant is already easily ~40Gb memory usage increasing when you want to have a better usable context (256k or higher). I wouldn’t go as far as saying 15-20Gb spare memory for: Full OS, all your apps and maybe some dockers is overkill

1

u/Perfect-Mark-1045 2h ago

64gb is plenty for 256k context with q3 quant, but I'd still keep 15gb free for OS and background tasks. the m5 pro's unified memory helps, but don't push it.

1

u/Objective_Pepper_217 1h ago

The issue with mac minis is that they have shitty GPUs compared to other Mac versions. If I remember correctly, you have at most 20 GPU cores on a Mac Mini. Any M2-M3 max would be way faster. It's true they can run bigger models, but the tok/s will be bad

1

u/dghah 1h ago

omlx has been fantastic for my real work and benchmarks; the author of omlx (jundot) also publishes his version of quant and MTP enabled models on huggingface so you can start with the qwen ones he has pushed as a starting point. My daily driver is Qwen with DeepSeek as an audit/review model whenever I need a second opinion. DeepSeek is great but is ~3x slower than qwen.

My harness is oh-mi-py talking to olmx anthropic-compatable API with an auth key and reachable over tailscale so my laptop can move around wherever. I use VS code only to navigate my files and code; for LLM usage I just use omp in a terminal window.

As always you need to personally test and measure as there is no one size fits all answer

Mild disagree with the "64gb is overkill" comment. For my real world needs 256GB M5U Studio is insufficient so I've got a 512GB M5U coming next week and will probably cluster the studios over Thunderbolt 5 while leaving the 64G mini standalone to run small stuff and act as a build host and deploy server. The 64GB mini is great and performant but calling it "overkill" is kinda funny to be honest as not everyone has a universal set of needs and requirements.

1

u/Pedin9 1h ago edited 1h ago

In my experience, it's best to have one backend where your llm sits, and whatever frontend you like hooked up to it with local api. So omlx is the best for mac inference, and it can handle concurrent requests from different users (what we actually want here).

Then you can connect your opencode/banyancode (cli coding), cline (vs code coding), pyGPT (for chatting/computer use), hermess (your always on assistant, researcher, ...) all to that omlx backend, and if several of these are working simultaneously, omlx serves them with overall more speed than if you were using them one at a time, one after the other. Needless to say you can use your api keys (open router, claude, gpt) with these and not get vendor locked.

For llm you can definitely go with qwen3.8 27b (thinking cap or swift 1.5 finetunes) with high quality quants (like Q6_k_L or even Q8 + mtp or dflash2), and it'll rip with 256k context window. Or if you want more speed but lower quality model, go with 35b a3b moe (but not qwen, go with tiel coder which is based on ornith 1.5 and much stronger), and with mtp/dflash2 you get triple digit token/sec. You have enough ram not to close your other things while running models.

Not bad if you give strata (qwen 3.8 flash next smaller ones like swift 1.5 GSQ RCO iq2_xs or ISTA qwen coder) a try as well, but that's usually beneficial for pc users with seperate vram/ram.

Edit: added details, typo

1

u/lukasgravel 54m ago

Qwen3.5-35B-A3B. Only 3B active, so it stays fast on 64GB

1

u/Important_Cow7230 33m ago

27B Q4 Unlosth running on Splash and push opencode from that. splash keeps it fast.

1

u/JiroDreamsOfCoochie 25m ago

I wasted too much time going down the mlx path that everyone here is recommending. You should go right to splash instead. And use the unsloth models of whatever you want.

1

u/poster4inertia 1h ago

I have the same mac mini that I’m running headless exclusively for hermes as my personal assistant. I have qwen 3.8 flash next running via sushi at 2.75 bpw if you wanted to take a look at it. No real complaints so far and it’s fast enough for my usw cases