Blackwell’s fifth-generation Tensor Cores accelerate distance computations (cosine, L2, inner product) 50x faster than CPU, enabling massive-scale similarity search on Milvus indexes.
Tensor Core Distance Operations
Vector distance calculations (dot products, norms, quantization transformations) map directly to Tensor Core operations. A single Blackwell GPU executes billions of distance computations per second. Milvus queries comparing 1M candidate vectors complete in microseconds.
Batch Query Processing
Tensor Cores process hundreds of queries in parallel using SIMD. Instead of serializing distance calculations across CPUs, Blackwell computes distances for all queries simultaneously. Query batches of 100-1000 complete in the same time a CPU processes a single query.
Quantization Transformations
Milvus uses quantized vectors for memory efficiency (1/8 original size). Dequantizing and computing distances on Tensor Cores happens at extreme speed. The speed advantage of quantization (smaller memory footprint) comes free with Blackwell acceleration.
Cross-Encoder Reranking
After initial retrieval, Milvus can rerank results using cross-encoder models (Tensor Core-accelerated). The combination of fast initial retrieval + GPU-accelerated reranking delivers better relevance than retrieval-only systems.