Zero-knowledge proofs (ZKPs) are cryptographic protocols that allow verification of statements without disclosing the underlying information. Among them, PLONK-based ZKPs are particularly notable for offering succinct, non-interactive proofs of knowledge with a universal trusted setup, leading to widespread adoption in blockchain and cryptocurrency applications. Nonetheless, their broader deployment is hindered by long proof-generation times and substantial memory demands. While GPUs can accelerate these computations, their limited memory capacity introduces significant challenges for efficient end-to-end proof generation.

This paper presents \textit{Pipelonk}, a GPU-accelerated framework for end-to-end PLONK proof generation with two key contributions. First, \textit{Pipelonk} introduces a segmentable operator library that offloads all operations, including those not trivially parallelized, to GPUs through new designs. Each operator supports segmented execution, allowing inputs to be divided into smaller segments processed independently, thus enabling large-scale computations on memory-constrained devices. Second, \textit{Pipelonk} provides a pipeline executor that overlaps computation and data transfer. It globally schedules compute- and memory-intensive tasks while preserving data and security dependencies, balances transfer-latency hiding against peak memory, and adaptively selects per-operator segment sizes by modeling memory capacity and computational characteristics to maximize compute-transfer overlap. Evaluation shows that \textit{Pipelonk} runs efficiently on devices with \SI{8}{GB} to \SI{80}{GB} memory, achieving an average speedup of 10.7$\times$ and up to 19.4$\times$ over the state-of-the-art baseline.