Blackwell enables Milvus to use higher-precision embeddings and more complex retrieval algorithms, improving RAG relevance without sacrificing latency or cost.
Embedding Precision Trade-offs
CPU-limited deployments force aggressive quantization (4-8 bits) to meet latency budgets. Blackwell’s performance allows full-precision embeddings (32-bit float) without latency penalties. Higher precision improves cosine similarity discrimination, reducing false-positive retrievals.
Multi-Stage Retrieval Pipelines
Blackwell enables two-stage retrieval: fast approximate search (recall-optimized) followed by reranking with cross-encoder models. Both stages execute on GPU. Final results rank by relevance rather than embedding similarity, improving downstream LLM generation quality.
Hybrid Vector-Keyword Search
Milvus can combine dense vector search with sparse keyword indexes on Blackwell. Queries match on both semantic similarity and keyword presence. Hybrid results capture documents missing from pure vector retrieval.
Contextual Chunk Scoring
Blackwell allows Milvus to compute relevance scores incorporating document metadata (recency, authority, domain). Complex scoring functions run at GPU speed, reranking retrieved chunks. LLM receives highest-quality context.