A deep dive into SilverTorch, the system Meta built to collapse recommendation retrieval — normally split across separate CPU services for approximate nearest-neighbor search, feature filtering, and scoring — into a single GPU-native PyTorch model under a new "Index as Model" paradigm. We cover the business problem (network overhead and duplicated infrastructure from stitching together microservices as retrieval architectures grew more complex), the technical approach (a model-based GPU Bloom filter for candidate filtering fused with an Int8-quantized approximate-nearest-neighbor kernel, plus unified scoring layers that replace what used to be separate services), and the reported production payoff: a 23.7x throughput improvement and 13.35x cost-efficiency gain over the prior CPU-based approach, now running across hundreds of retrieval models serving ...