This is a guest blog post co-written with Minghui Yu and Jianzhe Xiao from Bytedance.
ByteDance is a technology company that operates a range of content platforms to inform, educate, entertain, and inspire people across languages, cultures, and geographies. Users trust and enjoy our content platforms because of the rich, intuitive, and safe experiences they provide. These experiences are made possible by our machine learning (ML) backend engine, with ML models built for content moderation, search, recommendation, advertising, and novel visual effects.
The ByteDance AML (Applied Machine Learning) team provides highly performant, reliable, and scalable ML systems and end-to-end ML services for the company’s business. We were researching ways to optimize our ML inference systems to reduce costs, without increasing response times. When AWS launched AWS Inferentia, a high-performance ML inference chip purpose-built by AWS, we engaged with our AWS account team to test if AWS Inferentia can address our

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