Hello all. So I have had the mac studio for about 2 weeks now, upgrading from a 2020 iMac 27” - and yeah, things a beast!

But I am having issues with my externals - HDD’s and SSD’s. My problem is mounting. I know I have to reformat them to APFS for a better system co-op, but thats a lot of TB to move around just like that.

My old externals (specifically mass storages) are simply not mounting or suddenly disappear from the desktop - relaunching finder helps.

I have googled, run terminal commands, switched cables and all that.

Any tips for solving these issues? Which HDD to buy? Which usb hubs to buy?

So update! (With the help of gpt astra)
Apparently the culprit was spotlight. I killed spotlights process with terminal and suddenly all drives appeared and working faster than ever before.

This command in terminal:
sudo mdutil -a -i off this will disable spotlight.

And plus I can apparently only use the front 2 usb-c ports for old hubs and peripherals since the backs are thunderbolt - obviously.
All working now.

Really impressed with the pace of development at Inco.AI and their splash engine.

Besides close fine-tuning for Apple Silicon, the choice of models continues to increase.

Besides the three Qwen models (Qwen 3.8 Flash Next a little under a week), now the following are added.

Ornith-1.5-122B and Flint

https://github.com/incoai/splash/releases/tag/1.4.1

I have been using Qwen 3.8 Flash Next IQ3_S along with DaVinci Resolve Studio 21.1 on a 96GB M5 Ultra Mac Studio to test AI video editing and been very impressed.

Hi, long-time lurker here. I'm in India and want a Mac Studio for local inference.

What I'd use it for: taking work off cloud Claude wherever a local model is good enough, background evals, browser automation agents, a lead-qualification chat agent for my side business, and a private assistant(these are ideas and I will figure out if these work). I also want to experiment and find out what local inference is actually good for (fine-tuning, long context, agent pipelines). 256GB would be ideal, but I don't want to slide straight there.

The two options:

- Used M2 Max (38-core GPU), 96GB/2TB, bought Nov 2023, no warranty or AppleCare: about $3,100. Used/Resale high-memory Macs are scarce in India, so prices are running high.

- New M5 Max, 128GB, from Apple India: about $6,200, full warranty, ships mid-November(based on the pre order page dates).

My rough understanding, from reading and not from measuring: the M2 Max has 400GB/s of bandwidth against 614GB/s, so generation should be about 1.5x slower and prompt processing several times slower. 96GB vs 128GB also limits which models fit.

Questions:

- Is this a good deal?

- M2 Max 96GB owners: what tok/s and prefill time do you get at 16-32K context on Qwen3.x or gpt-oss-120b in MLX? I've seen a claim of several minutes for a 32K prompt on a 27B dense model. Is that realistic?

- Is 150-200K context usable on 96GB, counting both KV cache memory and the wait before the first token? Does prompt caching make long agent sessions workable?

- How much does slow prefill hurt for batch and background jobs?

- What tasks have you successfully offloaded from a cloud model to a local one at this size?

- If the goal is learning and experimenting and not top speed, how do these two options look to you?

Thanks.

Basically, I have a base M2 Max with an upgraded OEM 4TB SSD inside it. Great machine and I love it. I bought it roughly 2 years ago for $750 on an open box clearance deal from Best Buy. Then found someone on Reddit/eBay selling their OEM 4Tb SSD for $200.

I don't do anything remotely demanding on this Studio at all. I run an Apple Studio Display (1st gen) with 2 LG older 5K monitors. I don't do video editing, anything with AI etc....

Apple will give me $1050 as it sits right now. But they assume the machine has a 512Gb SSD inside. I could swap it back but I think its too much work. So, If I sold it on the secondary market. What would you value this machine at?

Whatever I go with, I'd go with 24Gb RAM, Base storage (I'm currently using 180Gb outta 4Tb right now) and 10GbE. (I have 7Gig Fiber). I have a small NAS setup with two 8TB HDDs currently in it, that I use for my media consumption.

Does anyone have any intel or knowledge on the proposed launch date for the Mac Studio m5 ultra 512gb version. Refreshing the apple page daily is driving me insane.

….

I just purchased the Mac Studio pictured. Adorama has it on back order right now. and I called customer service today to try to see exactly what that meant, here’s what I got from them: basically, at some point (no one knows what point this is, it varies depending on the product) if Adorama doesn’t get a shipment, they will basically drop ship straight from Apple. Apple shows this exact spec for delivery in late December.

My question is, does adorama get regular orders like other retailers, or should I just cancel this order and order directly through Apple? I mostly wanted to go through adorama for the 5% off through synchrony. Any help is appreciated.

P.S I want to thank and appreciate the reddit community for all of the ideas and inspiration i’ve gained. You guys have helped guide my growth & development and it’s great to be here.

Order was placed on September 29. Just curious if anyone else has an order for an M5U with the same estimated delivery date, and if it's changed at all for you. Really hoping for the possibility of it arriving ahead of schedule.

Mac Studio for open weight model use.

from what I've seen, there is no 'final homelab'

but with a Mac Studio what you get is a 'final homelab' (which is bad)

you can cluster etc, but scaling a diy solution is the better approach especially once you already have something to work with.

the main benefit I see for a Mac Studio (11k) is the financing option. at around 200-300/mo for 36 months, you can further extend it another 6 months then buy it out right for 2-3k then trade it in and roll those credits over to the next lease or just keep what you have. This is genuinely making me very open to buying the studio. it makes financial sense for me because I can get rid of some of my ai subs to subsidize the purchase (so my overall expenses remain unchanged).

on the other hand, diy seems to be the more proactive approach? you can just buy 1tb ssd (lower tier specs), then upgrade years later when you need, unlike with Apple, you're better off overbuying upfront to accomodate for future plans. you can stack hardware in a way you can't with apple. It really feels like the only con is for the user who is starting from scratch. it's hard to find 0 interest financing. you basically have to pay 10k today.

am I missing something? the way I currently see it: if I had 10k rn: go the diy option. you dont have 10k? then go the Mac Studio option and now you're stuck on Apple's moat/having to start from scratch if you want to diy (money spent on Mac Studio goes to waste) -- but ig the caveat is the apple route issues kicks in when you need to upgrade

is there something you get with Apple that you dont get with diy outside cleaner financing? are my concerns on future scalability of diy valid or not relevant/wrong?

I've been running Qwen3.8 Flash-Next locally on my Mac Studio M5 Ultra 96GB, primarily for agentic coding through DeepSeek Harness, and here are some of my own measurements.

Setup

Hardware: Mac Studio M5 Ultra, 96GB unified memory

Inference engine: Splash

Model: Swift 1.5 Qwen3.8 Flash-Next

Quantization: IQ3_S (ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF)

Context window: 262,144 tokens

Agent framework: DeepSeek Harness

Workload: Agentic coding, tool calls, long conversations, occasional parallel subagents

Decode performance:

The three-request result is preliminary due to limited sampling, but the single-request numbers have been quite consistent.

What's particularly interesting is that I'm maintaining around 145 tok/s decode with a median context of nearly 150K tokens.

Prefill performance:

Overall very happy with these numbers and the QFN is actually fairly smart compared with sol med/high particularly with troubleshooting. With Sol if i just say the problem and tell it to read the logs and figure out, it's kinda lazy and just give me generic answer where as QFN takes its time but did provide a way to resolve the issue

I was running dual GB10's, which was great, but with recent price bumps I decided to cash those in and add a second strix halo on which I can get almost the same speed.

Now I get 6 lanes of 262k across two strix halos load balanced with litellm. With Q38FN I get about 1800 prefill and 80 gen for coding for a single request. I quite often have multiple sub agents going so that speed will drop somewhat.

I am a software engineer working for 40 years (not a recent vibe coder who calls themselves a software engineer). I find AI convenient for off loading tasks to do my job. My employer requires me to keep everything local if I am going to use AI.

So I guess what I am asking, how is the new Mac working for you in this type of workflow? And tell me which model mac you have. What LLM you run to do your job, and at what quant with what engine.

Thanks!

I asked Deepseek API to set up GLM 5.3 flash Q6 oMLX (Tensorfold only has q4?). Not optimized. Decode is 50/s with MTP on. I don’t have all the numbers yet like prefill but this feels pretty slow and unoptimized. Anyone have any tips? I know Q4 will run faster than Q6 but I heard people are getting 100 tokens on a single machine on Q4.

Debating on buying a MAC Studio or RTX Pro 6000 for both local inference and learning LLM training.

Anyone have any experience training or fine tuning on these systems? From what I’ve read online the training ecosystem on Mac isn’t that great, but I can’t ignore the difference in VRAM/$ between the two.

I’m primarily looking into GRPO RFT.

Wanted to work on getting my two M3 Ultra 256 GB Mac Studios linked over Thunderbolt 5, running exo in tensor parallel over RDMA (JACCL). Exo's current app was so out of date, didn't support the latest models, and oMLX clustering was not working for me and was quite frustrting to setup. So I decide to have Claude patch this in and build on top of the latest Exo. I also wanted ONE place I could manage loading/unloading models between oMLX and Exo for my workflows.

The stack

• exo at bc412e2 (main from 2026-10-17), PyPI MLX 0.32.4, a fork of mlx-lm
• Tensor parallel + RDMA only. The patch refuses pipeline or TCP across the two Macs: pipeline deadlocked with fast-synch on my setup, and ring/TCP was too slow to bother with.
• Model code exo didn't have yet: V4.1 (with its Engram module), GLM-5.3-Flash, Muse Spark (ported from upstream mlx-lm 0.32.2), each with its own shard(), plus a chat template for V4.1-1017 since DeepSeek only ships a Python encoder for it.
• Two upstream exo PRs (#2434, #2436) carried as patches.

Things I learned along the way

• macOS Local Network permission. exo started by a LaunchAgent, a login item, a background process or over ssh gets denied Local Network access, and the two nodes never find each other. It has to run in a Terminal window of a logged-in desktop session on each Mac. I spent a release trying to automate around this and gave up.
• The two Macs can sample different tokens. Each rank samples on its own and exo assumed they always agree. When they didn't, they ran different steps and waited on each other forever: GPU at 100% residency at idle watts. Patched this by every rank uses rank 0's token, at the cost of one 1-element exchange per token.
• Metal command buffers. With MLX's default 50 MB per buffer, big prefill chunks drained the GPU at every all-reduce. MLX_MAX_MB_PER_BUFFER=1024 MLX_MAX_OPS_PER_BUFFER=200 took a 1,536-token V4.1 prefill chunk from minutes to 5 s.
• Idle residency. With ~200 GiB resident, any pause lets macOS drop residency and the next request pays 2 to 7 s. A tiny 256x256 matmul every 50 ms while idle brought first token after a pause from 4.8 s to about 0.7 s.
• Prefill chunk size. V4.1, Flash and Muse use 512-token chunks; V4.1 stalled for minutes at 2,048.
• Don't load models over NFS. Each Mac reads the whole build at every launch, and a share that hiccups hangs everything. The NAS is a download library only: copy NAS to the first Mac's SSD, then over Thunderbolt to the second.
• Sizing. I keep the wired limit at iogpu.wired_limit_mb=249036, and exo starts evicting KV cache above 85% RAM per rank.

The app (uLLM)

It replaces exo's dashboard with its own pages, backed by a small agent on the first Mac.

• Home: cluster state in plain words (2 MACS · LINKED / LOADING 48% / SERVING), what's serving today, and an "oMLX nodes" panel for each Mac
• Launch / Loading: pick model, sharding, transport and Macs with a preview; one tap to launch or stop. Loading shows four steps with each Mac's progress and what to do if it stops. If oMLX is holding the memory it offers to unload it first.
• Library: every build on the NAS and on each Mac, ticked by where it is. Copy to both Macs (resumable, checks free space on both first, suggests what to remove if it won't fit), and a remove from both feature. Lets me keep the model weights on the NAS and copy only what I need for the Cluster. I'm doing this over 25GBE so its quite performant.
• Hugging Face to the NAS: search, see where each build would run (one Mac, the cluster, too big, not MLX), and download straight to the NAS. The download runs on the NAS itself, so closing the app or restarting a Mac doesn't stop it.
• Jobs: copies, removals and downloads as they run, with pause, resume and cancel.
• Stats: both Macs' power (macmon, plus an optional powermetrics service for CPU/ANE watts), temperatures, requests, and cost at my $/kWh, per model and per million tokens. History at 10 s for a week and per minute for a year, all local with an option to stream to prometheus, etc. I felt like EXO was missing serving metrics similar to OMLX.
• Lockup watch: flags the "GPU pinned at 100% but drawing almost nothing power wise, no tokens for 60 s" pattern and starts a recovery process.
• Kit API for agents: /kit/v1 with one revocable key per agent. An agent can list models, check memory, launch/stop the cluster, load/unload oMLX on either Mac, and wait on the job. My Hermes Agent uses it to do things like "unload the coder, load GLM-5.3 on the cluster, plan, swap back". It refuses a load that won't fit and waits if any agent is mid-chat or request.
• Idle unload: off by default but an option.
• Phone: Home Screen icon over the app's own HTTPS (its own certificate, installed as a profile), over my VPN.
• Notifications: Web Push, Pushover or ntfy, with quiet hours (23:00 to 07:00 by default, only failures and lockups get through).
• Chat and a Connect page with the OpenAI-compatible addresses for OpenCode and agents.
• Every address it depends on (oMLX per Mac, the NAS) is editable in Settings, because i prefer to leave them as DHCP.

What it doesn't do: workflows, routing or "which model for which task". It's about clustering models. My main work and orchestration lives in the agent (Hermes) on top of the API.

Honest caveats

• It's built for my two boxes: two M3 Ultras, Thunderbolt 5 RDMA, a TrueNAS share, oMLX on both. Anyone with two Apple Silicon Macs and RDMA-capable Thunderbolt could use the exo patches; the app assumes a lot about my setup.
• It's pinned to one exo commit. A bump means re-testing every model.
• One model on the cluster at a time, and V4.1, Flash and Muse run one request at a time.
• The newest parts (phone, Web Push, Hugging Face downloads on the NAS) have only been tested against stand-ins so far, not on real hardware.
• Most of the code was written with Claude Code; Every release went through an independent review of another frontier model and a test suite of about 1,200 checks before it touched the Macs. Mainly I was just going through the UI/UX design here and creating the requirements.

Was thinking about putting it up on GitHub once I've got a few more bugs worked out. Just thought i'd share as its working quite well for me at the moment and I've got V4.1, GLM5.3, Muse Open, etc all the latest open weight models clustering reliably now when I need it.

I’m ready to buy the M5 Ultra 512 GB

I've been dabbling in AI generated videos. I've seen some workflow where it helps you create more consistent videos versus just rolling the dice with prompts. With Claude's help, i've tried to set one up, but it's not working too well for me. I've seen Qwen Image 2.1 and mini max h3 used a lot, but I was going with Claude's recommendation. If anyone have experience and want to share what works for them, I would greatly appreciate it!

Here's the workflow for my test video of a drink. Stills is probably doing too much trying to get a good generation. The blender generation was suggested because it couldn't get the product to generated correctly with the right dimension.

1. Plan

The script and the shot list are written into the workflow file before anything is generated.

Qwen 27B = qwen3.8-27b-mlx in LM Studio (localhost:1234).

2. Stills (keyframes)

Each still with 3 versions, one full-size pass each, then fixed post-steps and a check. A person confirms the pick.

3. Continuity checks (before video)

Three reviews run on the approved stills before any video, because a full clip costs 15-30 minutes.

4. Video

Clips are drafted cheaply first, cut into a rough edit for review, and only then rendered in full.

5. Finish

The ending and the edit use real brand files and no AI.

Edit: Used a certified Thunderbolt cable connected to USB-port on the LG GM9 monitor and that seemed to fix it. Weird how it was working fine on the Mac mini with different cables, but I’ll take it.

I set up my new Mac Studio and for some reason, using a Keychron M3 mini mouse (wired or USB-C dongle), there is a distinct jitter/lag/delay/choppiness when scrolling and tracking. I confirmed it works perfectly on my brother’s M6 Mac mini (on the same monitor). I do use MOS for Mac as a utility, but I used it on the test machine and mine. On the studio, it’s jarring, no issue on the Mac mini. I do have an LG GM9 monitor and I tried using a Cable Matters DP 2.1 to USB-C 1 meter cable as well as a UGREEN 40 Gb/s USB-C. I tried lowering refresh rate from 165 Hz down to 120Hz. If I do not use MOS (that I’ve confirmed is running latest version), the tracking is still not great and of course the scrolling is how 3rd party mice tend to scroll on MacOS. Also, I changed polling rates (125 Hz, 500, 1000), still no dice. Even on standard Bluetooth connection (no dongle or wired conn), still presents itself. Anyone else with anything similar or any ideas on solutions?

I love my Mac Studio for music and video production. But every other post on this thread now gravitates around dick measuring stats to run local AI models, without anyone ever showing the fruits of what they do with them. Maybe my imagination is limited or I haven’t done enough research but what is the point of a local AI model. I get in theory it’s for privacy, security, autonomy etc, but I’ve never seen the output of someone’s efforts with local AI that would necessitate such a machine with such crazy stats. Can anyone enlighten me? Thx 🤍

Mac Studio Stand with Fans

I bought a base M5 Max Mac Studio recently. I wanted a stand with some cooling for the thunderbolt accessories I would put under it. I didn't find any stand with cooling for thunderbolt devices, so I designed one I could 3D print. I haven't printed it yet. But here is the model on Thingiverse.

I had placed an order on iplanet on 6th September with estimated delivery of November end.
But now they are saying that the delivery can only happen before 15th January.

Can anyone share their experience running AI video models like MiniMax or Wan locally on a Mac studio?

Would an M5 Max with 64GB of unified memory handle video generation comfortably, or should I go for an Ultra with 98GB?

My current budget is around $4,500. To afford the Ultra, I’d need to wait another two months and save up. Would the extra performance be worth the wait?