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|`--docker`| — | off | Use Docker for [deterministic rendering](/concepts/determinism)|
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|`--quiet`| — | off | Suppress verbose output |
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## Workers
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Each render worker launches a **separate Chrome browser process** to capture frames in parallel. More workers can speed up rendering, but each one consumes ~256 MB of RAM and significant CPU.
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### Default behavior
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By default, Hyperframes uses **half of your CPU cores, capped at 4**:
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| Machine | CPU cores | Default workers |
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|---------|-----------|----------------|
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| MacBook Air (M1) | 8 | 4 |
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| MacBook Pro (M3) | 12 | 4 (capped) |
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| 4-core laptop | 4 | 2 |
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| 2-core VM | 2 | 1 |
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This is intentionally conservative. Each worker spawns its own Chrome process, so the per-worker overhead is significant. Fewer workers avoids resource contention with FFmpeg encoding and your other applications.
Start with the default. If renders feel slow and your system has headroom (check Activity Monitor / `htop`), try increasing `--workers`. If you see high memory pressure or fan noise, reduce it.
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</Tip>
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### When to use 1 worker
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- Short compositions (under 2 seconds / 60 frames) — parallelism overhead exceeds the benefit
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- Low-memory machines (4 GB or less)
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- Running renders alongside other heavy processes (video editing, large builds)
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### When to increase workers
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- Long compositions (30+ seconds) on a machine with 8+ cores and 16+ GB RAM
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- Dedicated render machines or CI runners
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- Docker mode on a well-provisioned host
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## Tips
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<Tip>
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Use `draft` quality during development for fast previews. Switch to `standard` or `high` for final output.
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</Tip>
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- Use `npx hyperframes benchmark` to find optimal settings for your system
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- 4 workers is usually the sweet spot for most compositions
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- Docker mode is slower but guarantees [identical output](/concepts/determinism) across platforms
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- For compositions with many frames, `--gpu` can significantly speed up local encoding
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