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2+ articles
AIAI inferenceAlibabaCarnegie MellonDatabricks

Perplexity AI Unveils Cutting-Edge Embedding Models, Challenging Google and Alibaba

Perplexity AI has released sensational new open-source embedding models, pplx-embed-v1 and pplx-embed-context-v1, setting new benchmarks in AI. These models not only surpass Google's gemini-embedding-001 but also nearly match Alibaba's wen3Embedding-4B—all while utilizing significantly less memory. With a bidirectional attention mechanism and efficient quantization for large-scale deployments, these models present a powerful tool available under MIT license on Hugging Face and the Perplexity API. Explore this new frontier in AI contributing to semantic search, RAG systems, and more.

Mar 3
Perplexity AI Unveils Cutting-Edge Embedding Models, Challenging Google and Alibaba

AI Quantization: Efficiency at the Edge, But Are We Hitting a Wall?

Discover the dual-sided story of AI quantization—a technique boosting model efficiency but now facing potential limits. As precision reduction in AI models enhances computation speed and size reduction, is the industry reaching its quantization ceiling? Explore the next steps in improving AI efficiency and alternative approaches on the horizon.

Dec 24
AI Quantization: Efficiency at the Edge, But Are We Hitting a Wall?

Related Topics

AIAI inferenceAlibabaCarnegie MellonDatabricksGoogleHarvardHugging FaceMITMeta

Most Read

1
Perplexity AI Unveils Cutting-Edge Embedding Models, Challenging Google and Alibaba
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AI Quantization: Efficiency at the Edge, But Are We Hitting a Wall?

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