Zero-Knowledge Proofs (ZKPs) have emerged as a powerful tool for secure and privacy-preserving computation. ZKPs enable one party to convince another of a statement’s validity without revealing anything else. This capability has profound implications across many domains, including machine learning, blockchain, image authentication, and electronic voting. Despite their potential, ZKPs have seen limited deployment due to their exceptionally high computational overhead, which manifests primarily during proof generation. To mitigate these overheads, a growing body of work has proposed hardware accelerators and GPU implementations for both individual kernels and complete protocols. Prior art spans a wide variety of ZKP schemes that differ significantly in computational overhead, proof size, verifier cost, protocol setup, and trust assumptions. Modern ZKP protocols are intentionally designed to balance these trade-offs. A particular challenge in contemporary ZKP systems is supporting complex, high-degree gates using the SumCheck protocol. We address this challenge with a novel programmable accelerator that efficiently handles arbitrary custom gates via SumCheck. Our accelerator achieves up to 1000× geometric-mean speedup over CPU-based SumCheck implementations across a range of gate types. We integrate this unit into zkPHIRE, a programmable full-system accelerator for the HyperPlonk protocol. zkPHIRE achieves a 1486× geometric-mean speedup over CPU and an 11.87× geometric-mean speedup over the state of the art at iso-area. Together, these results demonstrate compelling performance while scaling to large problem sizes (upwards of 2³⁰ constraints) and maintaining small proof sizes (4–5 KB).