Short answer: yes, a Mac generates AI video locally, and it is slower than you want and cheaper than you expect. Here is what the experience is actually like, with numbers we measured rather than numbers we were given.
How long a clip takes
A hero-quality clip is tens of minutes on a single M4, not seconds. One of our real renders — 1080×1920, 97 frames — took 1,830 seconds. That is half an hour for a few seconds of footage.
If you are coming from a hosted generator that returns something in ninety seconds, recalibrate now. The trade you are making is latency for cost and privacy, and it is only a good trade if your workflow can absorb the wait.
How much memory it needs
More than you would guess from the file sizes. That same render peaked at 44.1GB against a budget of 57.6GB on the machine running it.
Unified memory is why this is possible at all: the GPU addresses the whole machine, so a 64GB Mac holds a pipeline a 24GB discrete card would have to offload. It is also why the failure mode is nasty — MLX's allocations do not show up in any free-memory API macOS exposes, so the machine looks fine until the kernel kills the render outright.
Plan memory by job shape, not by watching Activity Monitor. We estimate cost per job before claiming it, and a worker that cannot afford a job releases it back to the queue for a bigger machine instead of dying on it.
One honest caveat: our memory coefficient is extrapolated from measured renders, not derived from the model internals. It is good enough to schedule against and we re-tune it from the sidecars every finished render writes. Treat it as a working estimate.
When more Macs help, and when they do not
Four Macs give roughly 4× throughput on a batch of clips — near-linear, because the jobs are independent and the queue is the only shared thing.
Four Macs give you nothing on a single clip. One render cannot be split across machines: the frames come out of one pass that holds the clip's whole latent state, so there is no frame range to hand off. There is no NVLink here and no tensor-parallel, and we wrote up why because it is the assumption everyone arrives with.
So the question to ask is not “how fast is one clip” but “how many clips do I need by morning”. If the answer is one, a Mac farm does not help you. If it is forty, it is transformative — queue them and sleep.
What it costs
$0 per render. Not metaphorically — there is no meter. You paid for the machines, the electricity is rounding error against a GPU rental, and the footage never leaves your network. For a marketing team iterating on the same shot twenty times, the absence of a per-second cost changes how people work more than the speed does.
The verdict
A Mac is a bad GPU and a good farm node: large memory, low power, already owned, and sitting idle every night. If your work is batch-shaped, the hardware is already in the building. Setting it up starts here.