Graph ANN dominates because recall–latency tends to beat IVF / LSH at realistic operating points. Jasper’s pitch is a 3-way merge: Vamana-style graph search, GPU-friendly quantized vectors, and batch-structured insertion.
Product-quantization-style distance paths often require codebook indirections. Jasper instead argues for a more contiguous, stream-like distance path inspired by RaBitQ-style regularity. Toggle between access modes and search stages.
Mock data illustrates the episode’s core claim pattern: Jasper beats GPU graph baselines on throughput at comparable recall while also shrinking memory. But note the caveat—multiple factors change at once, so attribution is not fully isolated.
The insertion story is concrete: each new vector searches, proposes edges, then sort-and-prune consolidates updates. Deletions, modifications, and search-during-update consistency are the visibly weaker parts of the evidence envelope.