Perplexity Details Its GPU Embedding Stack: How Ivy, Tulip and ROSE Serve pplx-embed
Retrieval quality in an AI search product is bounded by two things: how good the embedding model is, and how cheaply you can run it across an index.
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Retrieval quality in an AI search product is bounded by two things: how good the embedding model is, and how cheaply you can run it across an index.
This week, Perplexity Engineering team published Fast Embeddings on GPUs, an under-the-hood account of the second — the serving infrastructure behind pplx-embed and the ranking models used […] The post...
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Retrieval quality in an AI search product is bounded by two things: how good the embedding model is, and how cheaply you can run it across an index. This week, Perplexity Engineering team published Fast Embeddings on GPUs, an under-the-hood account of the second — the serving infrastructure behind pplx-embed and the ranking models used […] The post Perplexity Details Its GPU Embedding Stack:
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- This week, Perplexity Engineering team published Fast Embeddings on GPUs, an under-the-hood account of the second — the serving infrastructure behind pplx-embed and the ranking models used […] The post...
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