Cost effective deployment of vision-language models for pet behavior detection on AWS Inferentia2 | Amazon Web Services
Tomofun, the Taiwan-headquartered pet-tech startup behind the Furbo Pet Camera, is redefining how pet owners interact with their pets remotely. Furbo combines smart cameras with AI to detect behaviors such as barking, running, or unusual activity, and alerts owners in real time. At the core of this capability are computer vision and vision-language models that interpret pet actions from the video streams.
Originally, Furbo’s inference workloads were hosted on GPU-based Amazon Elastic Compute Cloud (Amazon EC2) instances. While GPUs provided high throughput, they were also costly because the always-on inference needed to support real-time pet activity alerts at scale. To reduce costs and maintain accuracy, Tomofun turned to EC2 Inf2 instances powered by AWS Inferentia2, the Amazon purpose-built AI chips. In this post, we walk through the following sections in detail.
Challenge: Reducing GPU inference cost for real-time vision-language models at scale
Running advanced vision-language models like Bootstrapping Language-image...
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