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This article details Perplexity's optimization of GPU embedding serving infrastructure, specifically how it reuses LLM prefill and decode kernels to balance cost and latency for vector search workloads. The engineering team leverages mature Hopper and Blackwell hardware while utilizing custom Rust gateways (Ivy) and inference servers (Tulip) to manage batching and load balancing efficiently. For AI practitioners, the key takeaway is that embedding inference has converged on standard GPU engines, with performance gains driven by runtime orchestration rather than novel model architectures.