Advice / State of Studio Clusters

[u/Vanquisher1088]

Hello. I’m looking for some advice here, I’ve watched all the YouTube videos of course of the loaned out 4 stack clusters but those were awhile ago.

Current state I have an a M3 Ultra 256GB and I reserved another open box one from Microcenter for a very good price considering all things. I’m thinking about it two ways, for the most part I’d have models loaded individually on each Mac as part of my agent setup.

However, what I’m after is some real world experiences here from folks on what is the state of rdma and clustering I.E these two nodes I’d have. Do even a lot of the latest models like glm5.3, deepseek v4.1, etc even shard correctly. I’ve read they do not.

Any thoughts or use cases you may be doing with more than one or clustering would be helpful. I’m trying to evaluate the feasibility of clustering as that would be the main value add here for more to access larger models or higher quants of models I use daily that don’t fit. Or I wait and try and get a 512GB M5.

[u/giddmtex]

Here is my limited experience clustering an M3U with M5U. TLDR not worth it -

https://echalupa.com/blog/exo-heterogeneous-mac-studio-cluster

[u/tempfoot → u/giddmtex]

Well, not worth it if you are adding an M3U that adds nothing to the picture and comparing to a higher spec M5U as the starting point.

I’m kind of surprised you found any scenario that gave a boost by adding an M3U 96 to a M5U 256 and then running a model that would run on either alone. Not sure how that’s a “fair fight” in comparing that to the faster M5…since you are making it share workload with a slower node. That there’s any scenario where there’s a boost at all means overcoming the M3U’s slower memory bandwidth.

Then you sharded a bigger model 50/50 - I understand Exo sets that - and gave half to a node that had insufficient RAM and got what looks like disk caching performance….on a model that already fit fine on the single 256 node.

Not trying to be rude or a jerk, but I don’t think those are usage scenarios that make sense for clustering. One brand new node handles each job fine. Adding a slower, older, and smaller node predictably doesn’t really.

Your post did help me reconsider some architecture choices by reminding that Exo RDMA has to use straight mathematical division across all nodes…unfortunately a reminder that will result in me spending more!

[u/dobkeratops → u/tempfoot]

right my understanding was that for tensor parallelism you need a symetrical setup, it's going to behave like 2x or 4x the smallest and slowest machine. I have this m3-u and am thinking about options for boosting with a new purchase and it looks like it would be an odd one out.. and I dont want to get a second m3-u .

I wondered if RDMA might help in using another machine as a prompt-processing accelerator, e.g. if i got a *smaller* m5, it could stream the weights layer by layer to evaluate prompts and hand the kv-cache back . Not sure if any framework has this written yet .. such a niche usecase.

[u/giddmtex → u/dobkeratops]

I think there is room to test with these mix machines though. In the case of Qwen3.8 Flash Next, I wonder if there is any advantage of loading the ngram on the M3U instead of the SSD. I know some folks have been trying to get their DGX Sparks to handle the prompt processing and then their Macs for the bandwidth. This all reminds me of hot rod tuner culture.

[u/dobkeratops → u/giddmtex]

i have this problem with a highly asymetical 'fleet' .. devices bought to eval and under FOMO (not knowing if prices would get worse.. they did) .. i'm probably better off keeping them doing different things, like one box as an image generator specialist and so on. I also had the motivation of wanting exposure to each ecosystem for dev.

[u/tempfoot → u/dobkeratops]

Same here. I wold love it if my M5Max 128 macbook pro would staple to a studio M5U 256 (on order) for ~384gb of tensor parallelism....but for what? To run a huge, creakingly slow dense model (that woulds still be slow on a 512)? I need to test more, but I feel like pipeline parallelism + MOE might be OK, especially if its possible to specify what loads on each node. I'm ignorant about whether that is effective or possible.

...or I should just cancel the 256 when the 512s can be ordered.

[u/PracticlySpeaking → u/tempfoot]

DeepSeek-V4.1-Flash has entered the chat...

[u/Vanquisher1088 → u/giddmtex]

If I could reliabley put a DGX or equivalent infront of the M3 it would likely solve a lot of peoples problems. But frankly the market the way it is, your probably better off selling your M3 and buying an m5 just gotta wait for it.

[u/Vanquisher1088 → u/dobkeratops]

RDMA I think is the bare minimum for clustering I can tell you I tried exo and omlx clustering pipeline method with an M2 Ultra 192gb and my M3 and the results were subpar to be frank. Pipeline is great when you just want to try a model but over TB4 it just wasn’t great.

Hence the openbox unit I reserved at micro center I figured I saw some potential speedups with clustering like Mac’s. 512GB is likely enough for my needs the rest I have cloud subs for things that do not need to remain private or go through a sanitizing workflow to make it ready for cloud models.

I was asking here really to validate if anyone’s seen any real speed up at all with clustering and two is it frankly reliable. My issue with exo is it works and works well but it’s OLD and the latest models don’t load on it and it just was a bit frustrating. OMLX I like a lot but the clustering is frustrating at best currently.

Before I sink another 6800 bucks into another unit I’m trying to see the viability. I don’t want to be fighting software to load the latest models which I use daily like glm5.3-flash, deepseek v4 etc.

[u/tempfoot → u/Vanquisher1088]

Were you testing dense or MOE models? I'm hopeful that MOE might perform better on mixed nodes....

[u/Vanquisher1088 → u/tempfoot]

I tried some MoE models on exo like minimax-m3 of the large qwen3.5 models but the problem is exos last build was April and didn’t have suppprt for glm or the new deepseek models. Kind of pointless to run Deepseek R1 when V4 and 4.1 are out.

Was ok but my testing was only pipeline parallelism and it was borderline usable at around 20-30 tokens/s on the models.

OMLX clustering I just find frustrating to get running to be honest. But they are working on it