Private retrieval
Compare models that encode content for search or rerank retrieved passages before generation. These are often smaller than the answering model, but index volume, candidate count and latency targets determine the useful system.
Selection approach
Treat embedding and reranking as separate jobs. Benchmark retrieval recall first, then measure whether a reranker improves answer evidence enough to justify the extra inference stage.
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3 current models
Showing 3 models
Qwen
The largest Qwen3 embedding checkpoint, designed for multilingual dense retrieval, classification, clustering and text matching.
BAAI
A multilingual embedding model that supports dense, sparse and multi-vector retrieval with 1,024 dimensions and inputs up to 8,192 tokens.
Qwen
An 8-billion-parameter multilingual reranker for scoring retrieved passages across more than 100 natural and programming languages.
No model matches that search and workload combination.
Model-to-hardware fit
Use the precise model version, numerical format and complete software pipeline intended for production.
Check whether it can load on one GPU or requires supported multi-GPU loading.
Allow for context, cache, batch, media encoders and concurrent users.
Measure quality, latency and stability on representative work.
Model weights are only one part of the system. Data access, runtime software, storage, monitoring and administrator handover also affect a reliable deployment.
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