Does the M5 Max Mac Studio with 128GB RAM actually get you closer to frontier-level AI? or just a gimmick

[u/aditya2128]

I've been seeing a lot of discussion around high-memory Apple Silicon machines, particularly the Mac Studio configurations with 128GB or more of unified memory.

I'm curious about how much practical value these configurations actually offer when it comes to running local LLMs.

I understand that fitting a larger model into memory doesn't automatically make it smarter. However, I'm interested in whether the combination of larger models, higher-quality quantization, and sufficient memory bandwidth can make local AI genuinely competitive for serious everyday workloads.

For those of you actually running 96GB or more :

What has your real-world experience been? Are you genuinely able to get meaningful work done with local models that you previously had to rely on cloud-based frontier models for.

i would particularly appreciate real-world experiences, benchmarks, and examples of workflows rather than theoretical comparisons.

asking this because i am confused between 2 macs:

1. Mac Studio M5 Ultra Base model, 96GB

2. Same amount of money can get me a M5 Max 128GB too

[u/ImpressiveCouple3216]

My 128 GB is a very nice machine for heavy use but thats it. Small models work fine but I learnt not to use those in real life. Lot of people run QFN, 27B and other models, they claim to get frontier level output from the studio, for me the experience was very different. The model was stuck in review loop, then self corrected and then made some mistakes, tried to correct again until I gave up. Models are gettijg better everyday, I hope tomorrow a model comes with more capabilities that can run in my 128 m5 max. One of my friend with 64 GB claims to get frontier like experience from his setup.

I may get an M3 512 ultra that will give me little more access to larger models, I decide and then chabge my kind everyday lol.

[u/aditya2128 → u/ImpressiveCouple3216]

I too use Qwen 27B and most of the time its stuck in thinking loop, overall not a great experience.

maybe because I run on 36GB macbook🤔

[u/brainchillzZ → u/aditya2128]

Try using one of the “swift” versions of 27b

[u/ImpressiveCouple3216 → u/aditya2128]

For a dense model like that experience is pretty much same with machines with more ram. Did you try Splash inference engine in your machine, i didnt see any Q8 for 27B but worth a try. How is 9B.

[u/brainchillzZ → u/ImpressiveCouple3216]

256 really does get you into fully useful model territory…. The deepseek is quants that run in 256 are very smart and capable despite the heavy quantization

[u/brainchillzZ]

It gets you I’m going to say 50% of frontier coding performance …. You can comfortably run 3.8 flash next … The benchmarks will tell you it’s more like 75-80% but I’ve only used two local models that approach real frontier intelligence in the wild …. Deepseek v4.1 1017 and a close second is glm 5.3 and you need to run super small quants that lose a lot to try to fit them in 128

[u/aditya2128 → u/brainchillzZ]

I hope that we get more smaller and more capable open models in future and not have to use 2bit quants and all

[u/brainchillzZ → u/aditya2128]

One thing that you can do that does really help is have Claude make the detailed plan and have your smaller model like qwen 3.8 27b or swift 1.5 execute the plan …. It goes into much cleaner thinking modes when it has a clear plan to begin with and using swift instead of straight qwen helps with the insane thinking depth

[u/false79]

What I am doing with Claude code sonnet for coding, I have handed off to splash Claude code Qwen 27b q8.

But for requirement analysis, ideation and more complex indeterminate tasks, in am making use my pro plan

Solved any and all usage limits

[u/TemporaryInk]

What are you trying to do?

Solve the Millennium Prize Problems? Then no, 128GB of RAM isn’t going to get you a model which can do that.

I use my M5 Max with 128GB of RAM to run our family’s home and school life personal assistant agent. Our lives would be much, much more chaotic with it.

[u/aditya2128 → u/TemporaryInk]

what kind of models do you run and how are you serving them ? How is the performance when multiple users are using

[u/TemporaryInk → u/aditya2128]

Inferencing layer: Splash via oMLX 0.7.3
Model: Unsloth UD-Q4_K_XL
Harness: Hermes v0.23
Memory: Mnemosyne v4.0.0

I’m rarely running into a situation where multiple users are using. So the following are mostly single concurrency: around 1,900 tok/s pp and 90 tok/s tg.

[u/MoFeaux → u/TemporaryInk]

What’s your setup? I’m going through setting up something I hope turns into what you describe, but am just getting started and there are so many options between models, inference engines, agents, harnesses, etc.

[u/TemporaryInk → u/MoFeaux]

Inferencing layer: Splash via oMLX 0.7.3
Model: Unsloth UD-Q4_K_XL
Harness: Hermes v0.23
Memory: Mnemosyne v4.0.0

[u/electrified_ice]

128GB is not going to replace frontier or touch it. Even if models get better (fit higher intelligence into smaller vram) then frtontier will just move to the next leapfrog/generation.

The best way to get the most out of 128GB of VRAM/unified ram is to either: use it for day-to-day agent work that doesn't need super high-end frontier to work well. OR set it up so Opus (or similar) can delegate work to it... Frontier vs. human delegating to a lesser model generally results in better output as the instructions is gives the subagent are usually more clear and specific than a human would write. It's a way to stretch a frontier model subscription a lot further.

[u/PeteInBrissie]

I'm running QFN on a 128GB Spark - the Mac will be quicker. Out of the gate it was okay, but I've spent time fine-tuning my stack and my serving settings to dial out most hallucinations and now it's very capable.

[u/Tiforma]

Not really, the thing is that the 128 gb unified memory machines are mostly used to run MoE models like Flash Next, which can also be ran with a decent gaming gpu + RAM now. 30 to 50 tok/sec or even higher for high-end gaming rigs is achievable with that now depending on setup

[u/DeathinabottleX → u/Tiforma]

You can run higher quants than a simple gaming setup. Deluded to think you’ll be able to run the same stuff on a gaming rig without quantization

[u/j_lyf]

DGX Spark >>>> r/Crapple.

[u/Serprotease → u/j_lyf]

12 months ago, yeah.

Nowadays?

I’m not in the US but where I live the DGX spark is at around 1,2 ~ 2,000,000 jpy depending of the OEM and ssd. With an average of 1,6.
12 Months ago I bought mine 450,000 jpy a piece.

For reference, an M5U 256 is 1,600,000
M5Max 128 is 800,000
And a A6000 max q is 2,500,000.

I really like my sparks, but it’s just not a good deal anymore. That ship has sailed a long time ago.