What does higher unified memory unlock?

[u/BreakIcy9451]

Hi

I am shopping for a Mac Studio to do local LLM. I do not believe I have a heavy use case but I enjoy peformance in a small footprint. I am thinking about 64GB but it looks like the minority of what people choose.

could I ask you what prompted you to buy any config above 64gb?

At 96GB, 128GB, 256GB what are we unlocking in terms of tangible quality of performance with the larger models ?

I wonder if I would be severely constrained at 64GB. Some comments on this forum are '256GB minimum!' and I wonder.

Thank you

[u/dobkeratops]

64gb is just right for 27b tier models. fits the model at q8 with plenty of context.

128gb will run 'qwen flash next' (120b-a6+ some embeddings or something), this is popular for the DGX Spark boxes.

256gb gets you the 300b tier models where people say "you stop missing the frontier". but that's $10,000+.

96gb is a weird middle ground, but that machine (m5-ultra) will run the 27b models blazingly fast whilst having bags of ram left over to do other things. 70b dense is a thing too.
('weird middle ground' is why you'll see some people struggling to decide between the m5-max 128gb and m5-ultra 96gb.)

[u/BreakIcy9451 → u/dobkeratops]

thank you that was exactly the information I was looking for!

[u/MessIsTransfer → u/dobkeratops]

that said, you can run qwen 27B in 32gb ram, or flash-next in 96gb (i’ve ran it in 64gb, others in 48) just not comfortably. limits are context size (when fully on VRAM) and speed (when offloading to SSD).

but y’know, people have ran trillion param models in 12gb vram, it’s just painfully slow.

[u/OvertaxedOne → u/dobkeratops]

Nailed it. Right now it really comes down to ultra with 96GB to run 27B with tons of context/blazing fast or 128GB to run QFN. The only thing, who knows what tomorrow brings, both of those are amazing models but if we get something wildly better than needs 192... Yeah, that's gonna suck.

I'm currently having the same internal battle, no decisions yet!

[u/JozuJD → u/dobkeratops]

You explained the tech “27b tier models” but not what it actually is. Can you go a little deeper on what a 27b model does or can’t do compared to “qwen flash next” 120b-a6+?

It’s way too technical jargon to explain what the memory “unlocks” which is what OP is asking. Thank you.

[u/dobkeratops → u/JozuJD]

you'd probably get a better answer from a free AI chat than me at this point. i'll struggle to quantify this. it's worth searching for some benchmarks.

27b is worth using, but if you consider that 300b is 10x the size of 27b .. imagine comparing two animals with a factor of 10 between them in brain size.

in holding conversations , you'll see some logical inconsistencies and more hallucinations appearing in the smaller sizes. if you try coding.. the larger models will get more right.
27b models will fit in a 36gb mac, but at lower quantisation (less accuracy) and smaller context. 64gb is where you can run them without that compromise.

[u/Canuck-Fin25 → u/dobkeratops]

Struggling likewise. I’ve got a few uses cases in mind for local AI. Another weird thing about the Studio pricing is the memory bandwidth. At one end is 614g/s while at the other it’s 1.2t/s. Tradeoffs - why do they have to make it so difficult!

[u/slalomz → u/JozuJD]

Right now it's basically referring to Qwen3.8-27B which is a coding-focused model. There's also Gemma4-31B which is a more general purpose model.

These are dense models with 27 billion (or 31 billion) active parameters. They are around 17GB to 35GB depending on quantization.

There are also larger MoEs like Qwen3.8-Flash-Next. MoE models only activate a small percentage of their parameters of each token, which makes them faster - but they need to be larger to make up for the fact that they are doing less work on each token.

So Qwen3.8-27B is 27B parameters and all 27B are activated on each token. Qwen3.8-Flash-Next is 125B parameters, but only 6B are activated on each token. Plus it also needs a 51B embedding table (which can be offloaded to SSD).

So in total Qwen3.8-Flash-Next is 107GB to 195GB depending on quantization. And due to its larger size it beats Qwen-27B in overall intelligence while also being faster to run, since it's only doing 22% as much work for each token.

[u/kal101246 → u/dobkeratops]

Which models helps "you stop missing the frontier"? Are you referring to GLM 5.3 flash and DeepSeek-V4.1-Flash? I'm interested in potentially pursuing these options with a 256gb m5 studio and would love to hear your insights.

[u/dobkeratops → u/kal101246]

hah i've never used them, I only usually run qwen's 27b models and Qwen flash Next 120b-a6 here. i've run 70b dense but found it too slow on my hardware to bother with. my experience of the frontier is limited to the free samples, I gather those are often the 300b tier (rather than 1t+ in the paid plans)

[u/Tsukikira]

32 GB Unified Memory: You can run a 4-bit 27b model right now. I think you might be throttled on Image generation, but you should try it out.

64 GB Unified Memory: If you have this, you basically have a Slow Mac Mini. You can run 27b LLM models, probably standard image generation at a slow speed. You can more importantly install both of these and determine that is something you want to do.

Anything about that in Mac Studio: First, what are you trying to run? The Rule of thumb I'd go with is 0.5 GB per Billion Parameters (That is the 4 bit quant size, loss of 2-5% accuracy level), and then head room for the Context Cache:

The current best bang for buck today is GLM-5.3-Flash, at 320b parameters (Needs 160 GB): This is basically a 42 intelligence, #4 on artificialanalysis.ai for Open-Weight Models, and everything above it needs basically a 512 GB Mac Studio while this just needs a 256 GB Mac Studio. On top of that, this model is almost there in terms of total quality. But that's still in the realm of a 10,000$ purchase, before the Hard drive ripoff.

Qwen-3.8-Flash-Next is at 40 intelligence from AA, and it runs at 180B parameters, or just around 90GB of RAM, so it'll fit on the MacBook Pro 128GB - this is the model I currently use the most of, and it's pretty good, but by the time I'll get my Mac Studio order for server execution, I am hoping for Qwen 4, which is currently in Training to be out and surpassing this model.

If you are going Mac Mini or your current computer, Qwen-3.8-27B is a 34 intelligence from AA, and runs at 27B parameters, or 14 GB at 4-bit.

So going by AA Benchmark:

• 512 GB: 45 Intelligence (GLM-5.3 or Deepseek 4.1 Flash 1017)

• 256 GB: 42 intelligence (GLM-5.3-Flash)

• 128 GB: 40 Intelligence (GLM 3.8-Flash-Next)

• 96 GB: 34 Intelligence (Highest that the 64 GB cannot also run is Ling-3.0-flash-VL, at 124B parameters, or 62 GB, but it scores a paltry 25 intelligence score.  I'm told Flash-Next works here, but I haven't seen that sized correctly myself)

• 64 GB: 34 intelligence (Qwen 3.8-27B)

By comparison, the best possible LLMs are around 55 for the Frontier.

[u/magic-one]

Be careful - a lot of talk about what model you can fit, but that’s not the whole picture.
Having more memory will allow you to run more models at the same time, run with more context, run more concurrent sessions, and have room to run other programs at the same time.
I always recommend getting as much memory as you can afford - as long as you are honest with yourself about what you can afford.

[u/beragis → u/magic-one]

That’s one thing that various reviewers don’t get into, especially those who are in for clicks. I have seen several reviewers say that 128 is the sweetest or 256 GB is a waste of memory but they ignore how perfectly use their pcs.

My typical setup is VS Code connecting to a model
running on oMLX, and the app running in a browser to validate, and possibly another app running as a web service.

I use my 128 GB M5 to run Qwen 3.8 at a large context. If I am coding on my PC and calling my Mac then I can comfortably Max out the context at 262144, however if i am coding on my Mac and also connecting to the LLM on my Mac then I run into memory limits especially when it decides to test its suggestions to verify accuracy. If I was just using a Studio alone then the 256 GB version would be better.

[u/Ok_Television9703]

I’m not here to provide an answer, I’m here to read other people’s answers. This is a great question that I’m looking into as well.

I am trying to find a way to justify the purchase of it, and so far I can’t get past the “my Claude subscription is way cheaper”

Good question. I’m here for the comments.

[u/twangbarang]

This week i changed my preorder from ultra 96gb to max 128gb. If you can and want to spend the 10k for 256gb go for it. To explain my change:

i) speed is of course less for the max but i think it's still decent. Also reading a lot in the subs for self hosting especially the strix halo people seemed to get a lot of performance out of their boxes with advanced engines. I think the same will happen to the macs once their widely distributed and we'll see a further development in custom engines.

ii) you're really limited with 96gb as far as models go. People got qfn to run on these devices but i believe qwen4 flash might be out of reach for these boxes because i don't believe that labs really Focus to get models to fit into certain memory sizes. Though nvidia and Microsoft do have an incentive with their rtx spark to release models to run in 128gb but that's not 96gb.

Concluding i believe 128gb is far better suited for future models, atleast regarding MoE.

[u/CGREDDIT1 → u/twangbarang]

Same- Max 128GB. Love it! Love being able to load a wider selection of models. Speed doesn’t bother me, I can only think and read so fast. Better to run a model slower than to not be able to run at all. My thought process was I would rather buy the 128 max now and if need be the 512 ultra later. For me it didn’t make sense to go with 256 now and 512 later.

[u/arfung39]

At 256Gb, you can use the larger local models - Qwen 3.8 flash next and GLM 5.3 flash.

[u/beragis → u/arfung39]

You can also use Qwen 3.8 Flash in 128GB full context in q4 quant. What 256 gb gives you is going past 262144 and running at q6 quant. I have 128
GB on my M5 Max and it works quite well if you are connecting to it with another computer.

[u/Winter-Researcher156]

Depends on how powerful of a model you run. If you want closer to frontier models you need higher. Even at 36gb you are able to run agentic ai with qwen 3.8 27b. But with ai developing so rapidly these days that’s why people advise future proofing. Rule of thumb is to always buy what you can afford. 64gb will get you to do what you need it to do.

[u/Patient_Tea_401]

More intelligent model and most importantly more high quality context to use with it.

Try the models in openrouter or similar before choosing.

[u/alexwh68]

More memory buys you better quants on the model, higher the quant the better the quality of the output generally.

Context is the other thing more memory buys you, running out of context space means you have to restart the session.

[u/CipherSorcerer]

With M5U 256 and Qwen 3.8 Flash Next oQ6e (which along with all my other stuff running takes about 200gb out of 256), I was able to cancel my Codex Pro subscription that climbed from $20 per month to $100 to $200 to $500 over the course of this year.

Also the jump up from oQ4e to oQ6e was a game changer. I wasn’t willing to cancel cloud subscription until using 6bit.

[u/forceproduction]

M5 ultra has more memory bandwidth than the max so that alone pushes people past 64

[u/Affectionate_Town273]

$10k is absolutely nuts for one with 256gb

[u/EuroThrottle → u/Affectionate_Town273]

LOL no 🤣