# TIGER Framework: A New Breakthrough in GPU-Accelerated Fully Homomorphic Encryption for Large Model Inference

> This article introduces TIGER, the first GPU-accelerated high-precision TFHE homomorphic encryption framework. Through programmable bootstrapping and batch processing design, it achieves order-of-magnitude acceleration on key nonlinear layers such as GELU, Softmax, and LayerNorm, providing a feasible solution for privacy-preserving cloud deployment of large models.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-06T15:54:35.000Z
- 最近活动: 2026-04-07T07:48:37.351Z
- 热度: 122.1
- 关键词: 全同态加密, TFHE, GPU加速, 隐私保护, 大语言模型, TIGER框架
- 页面链接: https://www.zingnex.cn/en/forum/thread/tiger-gpu
- Canonical: https://www.zingnex.cn/forum/thread/tiger-gpu
- Markdown 来源: floors_fallback

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## TIGER Framework: A New Breakthrough in GPU-Accelerated Fully Homomorphic Encryption for Large Model Inference (Introduction)

This article introduces TIGER, the first GPU-accelerated high-precision TFHE homomorphic encryption framework, which aims to solve the privacy issues in cloud-based large model inference. Fully Homomorphic Encryption (FHE) is the ultimate solution for privacy protection, but existing methods face efficiency and precision challenges when handling nonlinear layers. Through programmable bootstrapping and batch processing design, TIGER achieves order-of-magnitude acceleration on key nonlinear layers such as GELU, Softmax, and LayerNorm, providing a feasible solution for privacy-preserving cloud deployment of large models.

## Privacy Dilemma of Cloud AI and Challenges of FHE

Cloud AI services are convenient, but there is a risk of leakage of users' sensitive data. Fully Homomorphic Encryption (FHE) allows direct computation on ciphertexts and is a key technology to solve privacy issues. However, existing FHE schemes have shortcomings: CKKS has exponentially increasing overhead in high-precision nonlinear operations; although TFHE has a programmable bootstrapping mechanism, it lacks high-precision layer implementation and GPU parallel utilization, making it difficult to apply to nonlinear layers of large models.

## Three Core Innovations of the TIGER Framework

The TIGER framework has three core innovations:
1. WoP-PBS method: Decompose high-precision nonlinear functions into composites of low-precision subfunctions, breaking through the precision limit of lookup tables;
2. Implementation of key nonlinear layers: Optimize GELU (piecewise approximation), Softmax (numerically stable algorithm), and LayerNorm (reduce the number of bootstrapping operations) based on WoP-PBS;
3. GPU parallel design: Organize inputs in batches, use the multi-core architecture of GPUs for parallel computing, and optimize memory hierarchy to reduce transmission bottlenecks.

## Performance Evaluation of TIGER: Order-of-Magnitude Acceleration and High Precision

Performance evaluation shows that TIGER achieves significant acceleration on key nonlinear layers on GPUs: 7.17x for GELU layer, 16.68x for Softmax layer, and 17.05x for LayerNorm layer. At the same time, the error between encrypted inference results and plaintext is within an acceptable range, meeting practical precision requirements.

## Technical Significance and Application Prospects of TIGER

The technical significance of TIGER lies in proving that FHE can achieve high-precision and efficient computation for nonlinear layers of large models. Application prospects include: medical AI (diagnosis using encrypted patient data), financial analysis (risk assessment using encrypted data), enterprise document processing (summary and classification of encrypted documents), and cross-organizational collaboration (data is usable but not visible).

## Limitations of TIGER and Future Research Directions

TIGER has limitations: the computational overhead is still higher than plaintext inference, end-to-end encrypted large model inference has not been completed, and ciphertext expansion increases communication overhead. Future directions: optimize GPU kernels to improve throughput, combine model compression to reduce computation, and develop optimization schemes for specific scenarios.