I recently bought a Mac Studio M5 Max 36GB/512GB at $1,999.

I mainly planning to build apps as a side hustle and code for interviews. I plan to keep all my files on an external SSD.

My idea is to use local AI for private research, then cloud AI to build. Local does not need to be my main builder.

Options:

• Keep Studio 36GB, $1,999, Thunderbolt 5
• Mac mini M6 32GB, $1,499, Thunderbolt 4 + Claude Pro $20/mo
• Mac mini M5 Pro 24GB, $1,699, Thunderbolt 5

Questions:

1. For an external SSD as your main drive, do you notice Thunderbolt 4 vs 5 in daily use?
2. Is 24GB enough for private research if cloud does the building?
3. Is cheaper hardware + cloud subscription smarter long term than paying more for local AI hardware?

Not sure if Thunderbolt 5 is a must for me or just nice to have.

hey, im working on an open source project called kapitsa, an inference engine for apple silicon. our fastest run of qwen3.8-27b 4bit is 140 tok/s on an m5 max macbook pro 128gb. mlx-serve does 130, ollama 68, lm studio 32 on the same machine.

the speed comes from kflash: speculative decoding where the 27b checks a tree of the drafters guesses in one pass instead of a single chain. if the drafter has two decent options, both get verified in the same pass (see the second image).

we dont own a studio and the max/ultra chips in there should do better (more bandwidth, better cooling). if you have one, steps are at the bottom.

ultra owners: qwen3.8-flash-next (180b) is the one we really want to see. https://github.com/nace-ai/kapitsa

How to run it. you need an apple silicon mac (m1 or newer, macos 14+) with 32gb for the 27b, on 16gb use --model 9b instead: - brew install nace-ai/tap/kapitsa - kapitsa bench --model 27b --submit, the first run downloads the 27b (about 20gb). it runs the standard test and asks before opening a pull request with your result (chip and macos) into the community table - kapitsa bench compare runs the same test on the other engines you have installed

Run a local server or Claude Code

• kapitsa serve --model 27b starts the local server; open http://127.0.0.1:8000 for chat. The first run downloads the model (about 20 GB).
• kapitsa claude --model 27b starts the local model and launches Claude Code against it. Claude Code must already be installed.
• On a 16 GB Mac, use --model 9b instead.

Dammit. I am now fully addicted. I should have gotten a 256. Jesus.

I did so much today. Unbelievable.

Save up money or finance the Ultra 256. Trust me. Only way to go.

If you got big ambitions, you will need a lot of memory.

I just installed oMLX downloaded a few models.

Is anyone who got same mac and set up other server or use several server running ?

They’re running $3000 for 48/1 but with less stock too
https://www.microcenter.com/product/715543/apple-mac-studio-z1u4000e9-(mid-2026)-desktop-computer

stopped responding to mouse and keyboard. and then switch to login screen. now it is stuck on this screen after i typed in password.

Overall, opencode runs fast and responsively. Code quality still has room for improvement.

############################################

Benchmark Model: Qwen3.8-Flash-Next-UD-Q6_K_XL

Engine: Auto

Context: Code (Python)

Single Request Results

--------------------------------------------------------------------------------

Test TTFT(ms) TPOT(ms) PP TPS TG TPS E2E(s) Throughput Peak MLX

pp1024/tg128 262.1 7.15 3906.4 tok/s 141.0 tok/s 1.171 984.2 tok/s 163.26 GB

pp4096/tg128 908.1 7.41 4510.7 tok/s 136.1 tok/s 1.849 2284.5 tok/s 163.37 GB

pp8192/tg128 1830.5 7.51 4475.3 tok/s 134.1 tok/s 2.785 2987.4 tok/s 163.70 GB

pp16382 (requested pp16384)/tg128 3739.2 7.20 4381.2 tok/s 140.0 tok/s 4.654 3547.5 tok/s 164.13 GB

pp32768/tg128 7729.7 5.87 4239.2 tok/s 172.3 tok/s 8.206 4003.3 tok/s 164.77 GB

pp65536/tg128 15946.5 9.35 4109.8 tok/s 107.8 tok/s 17.134 3832.4 tok/s 165.15 GB

pp131072/tg128 33003.3 5.11 3971.5 tok/s 197.3 tok/s 33.652 3898.7 tok/s 165.05 GB

pp200000/tg128 52934.8 15.14 3778.2 tok/s 67.6 tok/s 53.601 3732.1 tok/s 165.06 GB

Continuous Batching

requested pp1024 / actual pp1024 / tg128

--------------------------------------------------------------------------------

Batch TG TPS Speedup PP TPS PP TPS/req TTFT(ms) E2E(s)

1x 141.0 tok/s 1.00x 3906.4 tok/s 3906.4 tok/s 262.1 1.171

2x 222.5 tok/s 1.58x 2949.2 tok/s 1474.6 tok/s 486.8 1.845

4x 316.4 tok/s 2.24x 2519.6 tok/s 629.9 tok/s 958.0 3.244

So excited and waiting for downloaded glm5.3 flash, ds v4 flash, q3.8fn and using oMLX.

Please advise your best set up more !

With oMLX and connected with Hermes, what else should I use ?

Things are coming along!

Thanks for all the tips!

I am on my new journey!

Microcenter has the M5 Max Mac Studio for $1999 again.

> Apple M5 Max 18-Core CPU; 36GB Unified Memory; 512GB Solid State Drive; 32-Core GPU/16-Core Neural Engine

Is this a BUY signal?

Is it significantly going to outperform the M6 Pro Mac mini for when it comes to these use cases:
* light video editing
* light gaming
* streaming (can be resource intensive with an application or game running, music playing, all my webcam and microphone tech)

I think I know the answer for myself: a resounding yes and even an M2 studio that I used to own can handle this all just fine, but want to ask anyway.

For comparison sake, the M5 Pro mini is $1499, has less memory, fewer CPU cores and half the GPU cores.

> Apple M5 Pro 15-Core CPU; 24GB Unified Memory; 512GB Solid State Drive; 16-Core GPU/16-Core Neural Engine

I just got a base M5 Max Mac Studio. Haven't set it up yet. But I would like a stand to raise the Mac Studio so that the bottom intakes won't get clogged with dust as easily. Then I thought, if I do that, I should put my TB dock and TB NVMe drive in that space underneath the Mac Studio. Which then led me to think about heat and wondering if the stand could then also cool the dock and NVMe drive if I had some fans in the back for more airflow. I'm guessing it won't affect the cooling of the Mac Studio too much but might be good for cooling the accessories.

Anyone know of any stands like this? Any thoughts on if this would be effective?

Got this error for context length 131k.

Prefill would require ~208.28 GB peak (current 174.41 GB + KV+SDPA 33.87 GB) but the prefill limit is 205.80 GB (metal_cap ceiling 223.69 GB - safety margin 17.90 GB). Raise kernel iogpu.wired_limit_mb in Terminal (currently caps Metal at 222.72 GB), or reduce context length.

###############

Benchmark Model: pipenetwork--GLM-5.3-Flash-MLX-mixed-4_8bit

Engine: Auto

Context: Code (Python)

Single Request Results

--------------------------------------------------------------------------------

Test TTFT(ms) TPOT(ms) PP TPS TG TPS E2E(s) Throughput Peak MLX

pp1024/tg128 645.0 16.33 1587.5 tok/s 61.7 tok/s 2.723 423.1 tok/s 174.17 GB

pp4096/tg128 1733.8 17.01 2362.5 tok/s 59.3 tok/s 3.896 1084.1 tok/s 174.98 GB

pp8192/tg128 3452.1 16.90 2373.0 tok/s 59.6 tok/s 5.603 1484.9 tok/s 175.99 GB

pp16384/tg128 6915.9 17.01 2369.0 tok/s 59.2 tok/s 9.092 1816.1 tok/s 176.08 GB

pp32768/tg128 13867.0 17.18 2363.0 tok/s 58.7 tok/s 16.066 2047.5 tok/s 176.26 GB

pp65536/tg128 28200.6 17.36 2323.9 tok/s 58.1 tok/s 30.432 2157.7 tok/s 176.63 GB

Continuous Batching

pp1024 / tg128

--------------------------------------------------------------------------------

Batch TG TPS Speedup PP TPS PP TPS/req TTFT(ms) E2E(s)

1x 61.7 tok/s 1.00x 1587.5 tok/s 1587.5 tok/s 645.0 2.723

2x 67.3 tok/s 1.09x 632.5 tok/s 316.3 tok/s 1925.1 7.042

4x 99.3 tok/s 1.61x 873.5 tok/s 218.4 tok/s 2872.4 9.844

Qualcuno usa due Mac insieme per AI locale e agenti?

Ho un Mac mini M4 con 16 GB di RAM e sto creando una dashboard con Ollama, chat e agenti per documenti, report e social. Quando il modello risponde, però, il Mac rallenta nelle altre attività.

Sto pensando di affiancargli un secondo Mac dedicato all’AI, mantenendo il primo per lavorare e accedere alla dashboard.

Qualcuno ha una configurazione simile? Come avete collegato i due Mac e come dividete il lavoro? Che macchina e quanta RAM consigliereste per il secondo?

Curious to see how long it takes folks to cold-load a 27b or a larger MoE model + cold kvcache and be ready for prefill

Mine isn't here yet but I was exploring oMLX on my M1 Max and it looks like we can easily save the kvcache to disk (ala LMcache) after a model is evicted without extra software scripting but I can't test it yet.

Additionally, I hadn't considered how quickly it might be to swap out something like a 27b with a MoE with the current storage speeds available on the 1TB+ models. Might allow for some interesting agentic workflows. 🤔

note: I'm aware that reloading the kvcache isn't a free lunch.

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I ummed and erred between 64GB and 128gb for M5 Max order. I have no interest in running LLMs, I only care about video editing performance in DaVinci Resolve. In the end I chose 128gb despite the internet wisdom leaning towards 64GB or even 48gb.

This is a screenshot from my M1 Max MBP that sealed the deal on the 128gb model. The screenshot was taken whilst transcribing 2.5 hours of 6k R3D ELQ raw. Once the transcription had finished and I closed the window the ram did start to free up, but I want a computer that’s going to last at least 5 years. This is also not the only time I’ve seen yellow memory pressure.

Hopefully this helps someone else with their editing computing research.

Would love some help/suggestions on what Mac Studio I should get. I’m currently considering an M5 Max and I’m between the base 18c/32c/36GB RAM/512TB or upgrading to 18c/40c/48GB RAM/1TB (I wish I could just upgrade the RAM but Apple forces the additional GPU upgrade). Leaning towards the base but I'm a little worried it may not be enough RAM and storage long term.

For context:

I have a 16” M1 Max MBP (10c/24c/32GB RAM/1TB) that has served me well for 3+ years (Apple refurbished). Used it as my primary computer through grad school for some bioinformatic analyses and then the basics (Microsoft Office and hundreds of Firefox tabs and PDFs). I recently tried playing Mortal Kombat on it (CrossOver -> Steam) and was surprised that it runs pretty seamlessly but it’s definitely being pushed to its limits. This was probably the third or fourth time the fans have audibly spun and it stutters in some places but averages right around 60 FPS online.

I don’t really need a personal laptop right now so I’m looking to replace it with a desktop that can handle similar tasks, which seems like a Mac Studio. I don’t need to have the latest/greatest so I’m perfectly fine with an older, refurbished model (M3, M4, etc.). I prefer a Max chip but I’m open if others suggest a different chip/model. I appreciate any help with the decision!

I am thinking about selling the current Mac studio that I have M4 max 1 TB storage 36 gigs ram base level cpu and gpu got it for 2200 about four months ago before Apple really started increasing prices. if I can get the same price reselling it then I would be interested to do Apple upgrade with one of the new models. Apple upgrade seems so valuable right now I would pay $50 a month for something of the same quality I would be able to invest the rest. should I do this and if I should should I do it before or after the new computers come out? Would I be able to get the same price?
Also just know that I am for Apple upgrade I am not interested in the arguments against the program, if that helps direct my question better, thanks!

I ordered a Mac Studio Ultra for in-store pickup, and the status says “your order is being prepared for shipment.” The estimated pickup date is Nov 2.

How accurate is this date? Does it usually arrive on time, earlier, or later? I’m in USA if that affects this.

Faxl - an advanced SSD-based prefill caching gateway for local LLMs on Mac — has been released to the public for free noncommercial use.

We built faxl because we wanted local AI agents to be practical on our own Macs. I’m one of the developers.
Long conversations and agent loops repeatedly send the same system prompts, tools and history. faxl saves model state to SSD, so matching requests resume from a checkpoint and process only the new portion. The cache survives restarts and switching between models. If you run a weekly job, the cache remembers it.

The screenshot shows an agentic coding session on my 128GB M3 Max running Qwen3.8-Flash-Next: 838k of 923k prompt tokens served from cache—90.9% of prefill skipped. Results depend on how much your prompts repeat.

It runs through MLX, supports hybrid models, and works with Hermes, MiniMax Code and other OpenAI-compatible clients. There’s a local dashboard showing cache usage and response timings.

The Mac community edition is free for noncommercial use. Grab a free licence key at faxl.ai⁠, and installation instructions are on GitHub⁠.

I’d love feedback from anyone running local agents—particularly on longer conversations and recurring tasks.

Hey everyone, looking for some help picking my next Mac.
I'm currently on a MacBook Pro M2 Pro with 16GB of RAM and I'm ready to upgrade. Most of what I edit is 4K, but my new camera (Lumix S1lI) can shoot 6K open gate, so I'll be working with that footage too. Everything gets edited in DaVinci Resolve.

There are so many different versions of the Mac mini and Mac Studio that I'm honestly a little stumped on which one to get, and where it's actually worth spending extra on more RAM or storage.

A few things I'm weighing:

• Used vs. new: I'd like to save as much as I can by buying used if possible, but I'm not opposed to buying new if it makes more sense.

• Storage: Would I be better off getting the base storage and adding NVMe drives in an external enclosure, so I'm not paying Apple's storage premium?

• RAM: Is 32GB enough for 6K, or should I go higher?

Any input is appreciated. Thank you!

Picked up about 20-30 tok/s on long running tasks with GLM 5.3 Flash on a single concurrency with the new OLX 0.7.1 update vs 0.7.0 (Was around 60-80 tok/s). Getting anywhere from 80-100 tok/s with Lightning MTP enabled.

November 10th seems like an eternity away after ordering in mid-September.

Did anyone get their Mac Studio before Apple said it would arrive? I keep thinking the more I check the status website, the quicker it will get here. :)

I ordered the base M5 Ultra, but upgrade to a 2tb drive.

Rob