Skip to main content
A Dual Mode orchestrator serves video transcoding and AI inference jobs from a single go-livepeer process. Video transcoding routes through NVENC and NVDEC, the fixed-function hardware blocks built into NVIDIA GPUs. AI inference runs on CUDA compute. Because these two execution paths use separate hardware resources, adding AI capabilities to a running video node requires no changes to your on-chain registration, staking, or existing transcoding configuration.
Dual Mode requires Linux. The AI Runner container supports Linux only.

​
What Changes

A video-only orchestrator and a Dual Mode orchestrator run the same go-livepeer binary. The difference is configuration: Dual Mode adds three flags and one aiModels.json file to your existing setup.

​
Before You Start

Confirm these prerequisites before starting:
  • Hardware: NVIDIA GPU with 16 GB VRAM or more. 8 GB is sufficient for LLM pipelines via the Ollama runner. Most diffusion model pipelines require 16 GB or more. See for per-GPU capability guidance.
  • OS: Linux. The AI Runner container is Linux-only.
  • Docker and NVIDIA Container Toolkit: Docker installed and the NVIDIA Container Toolkit configured so that nvidia-smi runs successfully inside a container.
  • For video transcoding: Your Arbitrum wallet is funded with ETH for gas, your node is activated via livepeer_cli, and you are staked with enough LPT to be eligible for the active set.
  • AI model weights: Model weights downloaded to your host machine before starting the AI Runner. See for download instructions.

​
Setup

Select the path that matches your current state.

​
Verify

After starting your node, confirm that both workload types are advertised to the network. Check local capability registration:
Check local capabilities
curl http://localhost:7935/getNetworkCapabilities | jq
The response includes a pipelines array listing your registered AI capabilities alongside your transcoding capability profile. A missing pipeline indicates a configuration error in aiModels.json or a container startup failure. Check external network visibility: Your node’s capabilities, including warm models, are visible externally at tools.livepeer.cloud/ai/network-capabilities. Search by service address or ETH account address to confirm the network sees the AI pipelines alongside transcoding registration. AI pipelines that remain absent externally for two to three minutes after startup usually point to a container startup or registration issue. Inspect the container logs:
Inspect AI container logs
docker logs livepeer_dual_orchestrator 2>&1 | grep -i "ai-runner\|container\|pipeline\|error"
For a full error reference including AI runner startup failures and capability registration issues, see .

​
Resource Management

Understanding how your GPU handles both workloads prevents VRAM-related failures.

​
Why contention is lower than expected

NVIDIA GPUs contain dedicated NVENC and NVDEC silicon for video encoding and decoding. These hardware blocks are separate from the CUDA execution units used for AI inference. Video transcoding in go-livepeer routes through NVENC and leaves CUDA cores for AI work. The VRAM constraint for a Dual Mode node is set by AI model weights.

​
GPU class and viable AI pipelines

The table below maps common GPU classes to AI pipeline combinations that are viable for Dual Mode. Transcoding capacity is available on all cards listed regardless of which AI pipelines are running.

​
Earnings

Dual Mode operators earn from two revenue streams with independent demand patterns. Transcoding fees track network video demand. AI inference fees track per-capability demand from application developers. Both are paid in ETH via probabilistic micropayments. Current per-pipeline AI pricing and per-orchestrator earnings are live on and . Market rates shift as network supply and demand change, so third-party figures age quickly.

AI Pipelines

Full aiModels.json schema, per-pipeline VRAM requirements, model download commands, and warm model management.

Gateway Relationships

How gateways discover your capabilities and select your node for both video and AI jobs.

Earnings and Pricing

Setting competitive prices for video transcoding and AI inference to maximise job volume from both revenue streams.

Orchestrator FAQ

Common errors and fixes: AI runner startup failures, VRAM out-of-memory, and capability registration issues.
Last modified on March 16, 2026