# Perplexity — Fast Embeddings on GPUs

- Company: Perplexity (perplexity.ai)
- Announced: 2026-09-04
- Category: not stated
- Coverage: not counted
- Announcement: yes
- Group: announcements
- Source: https://www.perplexity.ai/hub/blog/fast-embeddings-on-gpus
- Record: https://forck.live/items/10756-fast-embeddings-on-gpus
- Subject: Perplexity
- Models affected: pplx-embed

Fast and accurate search is vital to all of Perplexity, from Search and Computer to our API Platform. Behind the scenes, the heavy lifting is done by embedding and ranking models, which help our systems identify the most relevant results for a given query. We achieve state-of-the-art quality and latency by training and serving our own models, such as pplx-embed . This article presents an under-the-hood view of Perplexity's serving infrastructure for this special class of models. We discuss our techniques to efficiently address the inference needs of AI-native search, enabling quick prototyping and evaluation of models while powering our exabyte-scale search index . These techniques collectively expand the Pareto frontier of search quality and efficiency, enabling us to serve agents and users with the best possible results at the lowest cost and latency. Embeddings for Search In a typical search setup, indexed documents are mapped to a high-dimensional vector space using an embedding model and stored in a vector database. By embedding a query using the same model, similar documents can be located by finding the vectors closest to that of the query. …

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Record: https://forck.live/items/10756-fast-embeddings-on-gpus
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