Compliance monitoring · Production

ByteDance processes billions of daily videos using multimodal LLMs on AWS Inferentia2

The problem

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.

First attempt

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.

Workflow diagram · grounded in source
1
Billions of videos scanned daily
trigger
“the platform efficiently scans billions of videos each day”
2
Multimodal input ingestion
ai_action
“taking multiple data modalities, including text, images, audio, and video”
3
Cross-modal attention and fusion
ai_action
“Cross-modal attention mechanisms facilitate information exchange between modalities, and fusion layers combine representations from different modalities”
4
Decoder generates content analysis
ai_action
“The decoder then generates output based on the fused multimodal representation, enabling a more nuanced and context-aware analysis of content”
5
Flag guideline violations
output
“identify and flag content that violates community guidelines, enabling a better experience for all users”
Reported 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.

Reported metrics
Inference cost reductionhalf
Videos processed dailybillions
Reported stack
AWS Inferentia2Amazon EC2 Inf2 instancesAWS NeuronNeuronCores
Source
https://aws.amazon.com/blogs/machine-learning/bytedance-processes-billions-of-daily-videos-using-their-multimodal-video-understanding-models-on-aws-inferentia2?tag=soumet-20
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

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 tools did this team use?

AWS Inferentia2, Amazon EC2 Inf2 instances, AWS Neuron, NeuronCores.

What results were reported?

Inference cost reduction: half; Videos processed daily: billions (source-reported, not independently verified).

What failed first in this deployment?

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.

How is this compliance monitoring AI workflow structured?

Billions of videos scanned daily → Multimodal input ingestion → Cross-modal attention and fusion → Decoder generates content analysis → Flag guideline violations.

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