Self-Hosted Video Generation
Running open video-generation models on infrastructure I control.
- Role
- Solo — inference and serving
- Status
- UnfinishedNot hosted
Why it exists
Generated shots in these pipelines run through a third-party video API, so the serving stack behind them is not mine to tune. Self-hosting moves that onto infrastructure I control. This one is in progress: I am optimizing inference, not reporting a result.
How it works
- Self-host open video-generation models instead of calling a third-party API.
- Working on inference optimization — FP8 quantization and serving-level optimizations.
- Benchmarking on rented B200 GPUs through RunPod.
- No performance claims yet. A figure gets published only once the same run repeats on a fresh machine.
Trade-offs
Self-host open models
Instead of Keep calling the third-party video API
Quantization and serving become things I can change. The cost is that uptime, model updates and GPU provisioning are now mine to handle, and the hosted API handled all three.
Benchmark on rented GPUs
Instead of Buy or lease dedicated hardware
The hardware is released between runs instead of sitting idle. The cost is that every benchmark starts from a cold machine and depends on whichever host the provider allocates, which makes runs harder to compare.
Limits and how they are handled
- Nothing here is measured yet, so no performance number is published.
- The figures worth measuring are already named; each is held back until a cold-start rental reproduces it.
Built with
- RunPod
- B200 GPUs
- FP8 quantization
Where to look next
The repository is private. Ask for a walkthrough.