What can your Mac do?
Local logging, un-hyped.
ClipLogger's AI runs 100% on your Mac — which means the speed is bound by your hardware, not our servers. Here's an honest look at how long it takes to log a clip on the machine you already own. No uploads, no cloud required.
Your Mac, per clip
Estimated time to fully log one rich clip with the default on-device model. Pick your Mac to highlight it — or look yours up if it's not in the list.
| Mac | Chip | Unified memory | Bandwidth | ~ Per clip | A 200-clip shoot |
|---|---|---|---|---|---|
| Mac Studiodesktop | M3 Ultra | 96–512 GB | 819 GB/s | ~2–4 min | ~8 hrs |
| Mac Pro · Mac Studiodesktop | M2 Ultra | 64–192 GB | 800 GB/s | ~2–4 min | ~8 hrs |
| MacBook Pro · Studiolaptop / desktop | M4 Max | 36–128 GB | 410 GB/s | ~3–6 min | ~13 hrs |
| MacBook Prolaptop | M3 Max | 36–128 GB | 400 GB/s | ~4–7 min | ~16 hrs |
| MacBook Pro · Studiolaptop / desktop | M1 / M2 Max | 32–96 GB | 400 GB/s | ~5–9 min | ~20 hrs |
| MacBook Pro · Mac minilaptop / desktop | M4 Pro | 24–64 GB † | 273 GB/s | ~6–11 min | ~25 hrs |
| MacBook Pro · Mac minilaptop / desktop | M2 / M3 Pro | 18–36 GB † | 150–200 GB/s | ~8–14 min | ~33 hrs |
| MacBook Air · iMac · minieveryday Macs | M1–M4 (base) | 16–32 GB † | 100–120 GB/s | ~14–25 min | ~2 days |
This is exactly why Rush exists.
A 200-clip shoot is an afternoon-to-overnight job on your own Mac. Hand it to Rush and it runs parallel across many GPUs — the whole directory logged and named in under 5 minutes, same models, nothing kept. Local for the quiet nights; Rush for the deadline.
The math behind one clip
Where those minutes come from — the real weight of a single clip.
The "robust" default — a ~30-billion-parameter vision model at 4-bit (~21 GB in memory). Frames-per-clip is growing as the models get hungrier, so treat this as today's floor, not its ceiling.
Reading the frames the big one
Pushing ~40,000 image tokens through the model. This is most of the wall-clock — it scales with your GPU's compute.
Writing the log
Generating <400 tokens of tags, names and notes. Fast — it rides your memory bandwidth.
Two different bottlenecks — which is why a chip's GPU cores and memory bandwidth both matter, and why more RAM just lets the model fit at all.
About these numbers. These are modeled estimates, not lab benchmarks — real times swing with clip resolution, how many frames the model samples, your quant level, and thermal headroom (laptops throttle under sustained load; desktops don't). They're anchored to published Apple-Silicon inference data and a real Qwen-VL-72B run on an M3 Ultra (~3.5 min for a single full-res image). Treat them as order-of-magnitude, not a stopwatch.