compliance_monitoring · media · workflow
ByteDance processes billions of daily videos using multimodal LLMs on AWS Inferentia2
ByteDance faced the daily challenge of processing billions of videos for content moderation across its platforms, but traditional AI models hit efficiency limits at this scale, and existing inference infrastructure was too costly.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Billions of videos scanned daily
The platform efficiently scans billions of videos each day.
Tools used
AWS Inferentia2Amazon EC2 Inf2 instancesAWS NeuronNeuronCores
Outcome
ByteDance deployed multimodal LLMs on AWS Inferentia2, achieving the ability to process billions of videos daily while cutting inference costs by half compared to comparable EC2 instances.
What failed first
Traditional AI models lacked the efficiency to handle ByteDance's video processing scale and could not integrate multiple input modalities into a unified representational space.
Results
Cost replacedhalf
Grounding & classification
Source type: technical build writeup
21 fields verified against source quotes.
anomaly detectioncomputer visionquality inspectionmetric backednamed customerproduction runtime claimedtools describedvendor confirmedworkflow describedmediasoftwarecost reductionthroughput increasetechnical build writeupcompliance monitoringmonitor detect alert