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Pinterest Boosts AI-Powered Search Speed Sevenfold With Nvidia Tech | PYMNTS.com
The new infrastructure, which was built with Nvidia, will support visual search, content understanding, safety systems and other products that rely on both images and language, according to the release. In benchmark testing, the infrastructure delivered 85 times faster response startup and 7.3
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Pinterest Collaborates With Nvidia to Develop Multimodal AI Infrastructure
Pinterest, Inc. (Pinterest) offers visual search and discovery platform. The Company’s primary service, Pinterest, can be accessed through its mobile application or the Web. People use Pinterest to find ideas. As they browse Pinterest content, Pins, they fine-tune their tastes and find the idea.
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Pinterest Builds New AI Layer With Nvidia Tech
Pinterest has built a new AI layer using Nvidia's technology, which will allow the social-media platform to build AI products without creating custom infrastructure for each one. Pinterest plans to offer more products that rely on both images and language, including visual search, content
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Pinterest partnered with Nvidia to build multimodal AI infrastructure that delivers 7.3 times faster search performance using Nvidia Blackwell GPUs and Dynamo software. The system processes over 80 billion monthly searches while enabling Pinterest Assistant to handle 25 times more visual context per request.
Pinterest has deployed new multimodal AI infrastructure built in collaboration with Nvidia that accelerates AI-powered search performance by 7.3 times compared to previous systems
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. The infrastructure combines Pinterest's visual embeddings with Nvidia's accelerated computing platform, including Nvidia Blackwell GPUs and Nvidia Dynamo, to power visual search and discovery experiences for the platform's 553 million monthly active users2
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Source: PYMNTS
Benchmark testing revealed the system delivers 85 times faster response startup and significantly reduced latency by utilizing precomputed visual representations rather than repeatedly processing raw images
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. This technical advancement allows Pinterest to process its more than 80 billion monthly searches into high-value signals for AI-powered discovery and the AI shopping platform3
.The new AI infrastructure enables Pinterest to build AI products without creating custom infrastructure for each application
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. This unified approach supports visual search, content understanding, safety systems, and other products that rely on both images and language processing. Pinterest Assistant, the platform's AI shopping agent, can now run 25 times more visual context per request, substantially expanding its analytical capabilities1
."Building the next generation of AI-powered discovery means investing in infrastructure that can keep up with the scale and complexity of Pinterest," said Kartik Paramasivam, chief architect at Pinterest. "Our collaboration with Nvidia helps us deliver faster, smarter and more personalized experiences for the hundreds of millions of people who use Pinterest"
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The deployment represents the culmination of nearly five years of collaboration between Pinterest and Nvidia across more than 14,000 GPUs
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. This sustained partnership has positioned Pinterest to transform visual discovery with AI at scale. Ujval Kapasi, vice president of AI & HPC Frameworks and Libraries at Nvidia, noted that "Pinterest is transforming visual discovery with AI, helping hundreds of millions of people find inspiration through more intelligent and personalized experiences"1
.Pinterest's strategic focus on AI infrastructure aligns with recent business performance. The company's revenue topped $1 billion for the fourth consecutive quarter, while monthly active users reached record highs. Every pin users see now runs through a recommendation model trained on individual saves, searches and boards, according to Pinterest CEO Bill Ready
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. This indicates Pinterest is successfully reinventing itself as an AI-driven shopping destination, with one of the most complex AI systems in social media powering personalized discovery at massive scale.Summarized by
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