Skip to main content
This is not model hosting in the Hugging Face sense. You are hosting an inference service, not a model artefact. The distinction matters - see How Livepeer routes by capability, not model below.

​
What BYOC is (and isn’t)

BYOC (Bring Your Own Container) lets you run your own AI inference server inside a Docker container on a Livepeer orchestrator, and the network treats it as a callable AI capability. Livepeer does not restrict you to a fixed model catalogue or pre-approved models. Technically, any Hugging Face model can be containerised and run via BYOC. But Livepeer is optimised for low-latency, GPU-bound, real-time inference - especially for video and vision workloads. Models that violate these assumptions will be inefficient, poorly routed, or uneconomic. Rule of thumb: If the workload is frame-based or stream-based, it fits Livepeer well.

​
How Livepeer routes by capability, not model

Livepeer intentionally avoids model marketplaces, model-branded APIs, and centralised catalogues. Instead, it routes by capability descriptors:
  • image-to-image
  • video-to-video
  • depth
  • segmentation
  • style-transfer
Your orchestrator advertises capabilities, not model names. Gateways route on capability, price, and performance - not on which Hugging Face weights you load internally. This means:
  • Models can be swapped or updated without breaking downstream apps
  • No vendor lock-in at the model layer
  • Performance-based competition between orchestrators
  • Apps never need direct knowledge of which model runs their job

​
Implementation patterns

​
Pattern A - Real-time diffusion

Best for style transfer, image-to-image, live video effects.
  • Hugging Face SD / SDXL weights
  • StreamDiffusion or ComfyUI-style pipelines
  • Frame-in → frame-out processing
  • Persistent GPU residency

​
Pattern B - Vision utility node

Best for sub-tasks inside larger video pipelines.
  • Depth, segmentation, or pose models
  • Extremely fast per-frame inference
  • Used as conditioning steps feeding into diffusion

​
Pattern C - Hybrid pipeline

Best for differentiated orchestrator offerings.
  • Vision model output feeds conditioning into diffusion
  • Vision → condition → generation chain
  • Strong competitive differentiation in the marketplace

​
Hard constraints

Ignoring these will degrade routing priority and reduce job assignment:
  • Cold starts reduce job assignment. Keep models warm. Containers that take >10s to serve first inference will be deprioritised.
  • Excess VRAM usage limits parallelism. Efficient memory management means more concurrent jobs per GPU.
  • Slow endpoints are deprioritised. Gateways track latency per orchestrator and route accordingly.
  • Stateful jobs break retry and failover semantics. The network assumes short, repeatable units of work. Long-lived state breaks this.

​
Setup

​
Pricing and discovery

  • Set pricing per request, frame, or second
  • Pricing is advertised off-chain
  • Settlement occurs via Livepeer tickets
  • Gateways discover and route to you automatically
  • Applications never interact with Hugging Face or your orchestrator directly

​
See also

ComfyStream

ComfyUI-based pipelines for real-time video AI - node graphs, plugins, and gateway binding.

Workload Fit

Decide whether your model or use case belongs on Livepeer before you build.

Model Support

Full model family compatibility matrix for ComfyUI on Livepeer.

Hosting Models on Orchestrators

The operator-side view: how GPU node operators host and advertise models.
Last modified on March 9, 2026