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The paper is a systems-level step toward practical encrypted AI: a GPU framework that runs fully homomorphic encryption (FHE) inference on BERT-Base and Llama3-8B prefill, with reported timings of 8 s and 43 s. The performance claims (up to 2.25x over expert GPU libraries, parity with the CraterLake ASIC) are self-reported from the abstract and still leave encrypted LLM inference orders of magnitude slower than plaintext, so it matters mainly for privacy-sensitive deployments. Readers should watch whether the open-source release and independent benchmarks confirm these numbers.
Cerium: A Multi-GPU Framework for Terabyte-Scale Encrypted Inference
arXiv:2512.11269v2 Announce Type: replace-cross Abstract: Encrypted AI using fully homomorphic encryption (FHE) enables inference directly over encrypted queries, providing strong privacy guarantees. However, its computational and memory overheads have limited practical deployment. Custom FHE accelerators improve performance, but rely on advanced manufacturing technologies that limit their accessibility. GPUs offer a more widely available alternative, yet achieving ASIC-class performance on GPUs is challenging. Large models such as LLMs compound these challenges by requiring optimized kernels, terabyte-scale memory management, and efficient execution across multiple devices. We present Cerium, a multi-GPU framework for large-scale FHE inference. Cerium combines a domain-specific language, an optimizing compiler, and a runtime system to automatically generate optimized GPU kernels, compress plaintext data, manage terabyte-scale memory footprints, and optimize multi-GPU communication. Implemented on NVIDIA GPUs, Cerium outperforms expert-written GPU libraries by up to 2.25x and achieves performance competitive with state-of-the-art FHE ASICs, matching the CraterLake ASIC. It is the first GPU system to execute CKKS bootstrapping in under 10 milliseconds, achieving 7.5 milliseconds, and is the first to demonstrate encrypted inference for BERT-Base and Llama3-8B prefill in 8 seconds and 43 seconds, respectively. The Cerium framework is open-source and available at https://github.com/CMU-CAOS/Cerium
Abstract:Encrypted AI using fully homomorphic encryption (FHE) enables inference directly over encrypted queries, providing strong privacy guarantees. However, its computational and memory overheads have limited practical deployment. Custom FHE accelerators improve performance, but rely on advanced manufacturing technologies that limit their accessibility. GPUs offer a more widely available alternative, yet achieving ASIC-class performance on GPUs is challenging. Large models such as LLMs compound these challenges by requiring optimized kernels, terabyte-scale memory management, and efficient execution across multiple devices. We present Cerium, a multi-GPU framework for large-scale FHE inference. Cerium combines a domain-specific language, an optimizing compiler, and a runtime system to automatically generate optimized GPU kernels, compress plaintext data, manage terabyte-scale memory footprints, and optimize multi-GPU communication. Implemented on NVIDIA GPUs, Cerium outperforms expert-written GPU libraries by up to 2.25x and achieves performance competitive with state-of-the-art FHE ASICs, matching the CraterLake ASIC. It is the first GPU system to execute CKKS bootstrapping in under 10 milliseconds, achieving 7.5 milliseconds, and is the first to demonstrate encrypted inference for BERT-Base and Llama3-8B prefill in 8 seconds and 43 seconds, respectively. The Cerium framework is open-source and available at this https URL
Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
arXiv:2512.11269 [cs.CR]
(or arXiv:2512.11269v2 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2512.11269 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.1145/3830418.3843864 Focus to learn more DOI(s) linking to related resources
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