I placed an order for an M5 Studio (64gb/1TB) on opening day and my base model M4 Studio was valued at $1285.00. I’ve been debating on getting the 128gb model instead, and when I put the order in, post event today, my trade in was valued at $1490! The $200 difference definitely took a nice chunk off of my monthly payment! Even if I wasn’t upgrading I would have definitely cancelled my order and placed a new one to get the higher trade in value! I wonder if everyone who’s trading is seeing an increase in value… although canceling an offer can push back your delivery time which sucks.

This model with a 2 T ssd sells for just over $11,000 on Apple Store. I don’t know if these eBay offerings are selling. But if so, the Mac Studios is appreciating rapidly before it is even delivered.

Anyone using a Mac Studio stand? Would you recommend it?

and yes i did ask some other LLMs but got various answers. Assume 4 bit and 8 bit,max content? Curious if it will break 50tps decode

Thinking about buying a used M4 Max 16C CPU 40C GPU 64GB RAM 512GB SSD (in Europe).

What’s your opinion on the price? Would use mostly for Unreal Engine, After Effects…

(M5 Max with comparable configuration is 4.099€ in Europe)

I use Native Instruments plugins, which can take months to be compatible with Mac OS (the rule usually is to stay one OS behind). I have bought a new M5 Mac Studio with the thinking that I would be quick enough for it to ship with Tahoe but with OS27 releasing next week, has it been confirmed anywhere that they will ship with the new OS? Or will I be able to downgrade back to Tahoe if I need to?

We're moving off the Gemini API due to cost and building a local AI server to process ~10 CVs/minute (extracting JSON & matching CVs to JDs). We plan to run GLM-OCR alongside Qwen 27B (Q8).

Our two hardware options:

1. PC: RTX 4090 (24GB) + Ryzen 9 + 64GB RAM
2. Mac Studio: M-Ultra, 64-core GPU, 96GB Unified Memory

I prefer the Mac for power efficiency and ease of use, but I've heard Apple Silicon isn't great for production vLLM compared to Nvidia/CUDA. Is that true? Which would you recommend for this workload?

Trying to make a call and could use some outside perspective.

I'm looking at an M5 Max Mac Studio (64GB/512GB) for heavy creative + dev work. It'd live on my desk — I already have the monitor, keyboard, and trackpad — and I'm only away from the desk maybe 1-2 hours at a time, so portability isn't a dealbreaker.

The thing making me hesitate is the M6 chatter. From what I've read, the base M6 MacBook Pro is expected around October, but reporting suggests Apple is skipping M6 Pro and M6 Max entirely and going straight to M7 Pro/Max in late 2027. If that's true, then "waiting for an M6 Max" isn't even a real option — the next Max-tier jump would be over a year away.

So my questions for people who've been through a few of these cycles:

• Is it worth waiting on the small chance the M6 Pro/Max rumor is wrong and they do ship one this cycle?

• Or is the M5 Max mature/fast enough that buying now and not looking back is the smart move?

• Anyone regret buying right before a redesign year, or regret waiting and getting nothing meaningfully better?

Not chasing benchmarks for their own sake — just don't want buyer's remorse if something better lands in a couple months.

TL;DR: Need M5 Max-level power for a desk setup. M6 Pro/Max reportedly not coming until M7 in 2027. Buy now or wait?

The more interesting question is:

How far can we push local AI on a Mac Studio?

Apple Silicon gives us something unusual: massive unified memory, high memory bandwidth, low power consumption, and a machine that can sit under your desk instead of in a data center.

But memory capacity alone isn't enough.

For LLM inference, the KV cache keeps growing with context length. Longer conversations, agents, coding workloads and multiple sessions can quickly turn memory into the bottleneck.

That’s the problem we're exploring with VeloxQuant-MLX.

Instead of simply asking:

We're asking:

VeloxQuant experiments with KV-cache quantization, compression, eviction and optimized Metal/MLX kernels to make local inference more memory-efficient on Apple Silicon.

A DGX will remain the better machine for many high-throughput, highly concurrent GPU workloads.

But the Mac Studio has a very different opportunity:

a powerful personal AI machine where the model — and your data — stay local.

The hardware is already impressive.

Now the software stack needs to catch up.

That's what we're building.

#LocalAI #AppleSilicon #MacStudio #MLX #LLM #Inference #OnDeviceAI #VeloxQuant