Tilus: A Virtual Machine for Arbitrary Low-Precision GPGPU Computation in LLM Serving

• URL: http://arxiv.org/abs/2504.12984v2
• Date: Fri, 25 Apr 2025 18:40:48 GMT
• Title: Tilus: A Virtual Machine for Arbitrary Low-Precision GPGPU Computation in LLM Serving
• Authors: Yaoyao Ding, Bohan Hou, Xiao Zhang, Allan Lin, Tianqi Chen, Cody Yu Hao, Yida Wang, Gennady Pekhimenko,
• Abstract summary: Serving Large Language Models (LLMs) is critical for AI-powered applications but demands substantial computational resources.<n>Low-precision computation has emerged as a key technique to improve efficiency while reducing resource consumption.<n>Existing approaches for generating low-precision kernels are limited to weight bit widths that are powers of two.
• Score: 12.068287973463786
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Serving Large Language Models (LLMs) is critical for AI-powered applications but demands substantial computational resources, particularly in memory bandwidth and computational throughput. Low-precision computation has emerged as a key technique to improve efficiency while reducing resource consumption. Existing approaches for generating low-precision kernels are limited to weight bit widths that are powers of two and suffer from suboptimal performance due to high-level GPU programming abstractions. These abstractions restrict critical optimizations, such as fine-grained register management and optimized memory access patterns, which are essential for efficient low-precision computations. In this paper, we introduce a virtual machine (VM) designed for General-Purpose GPU (GPGPU) computing, enabling support for low-precision data types with arbitrary bit widths while maintaining GPU programmability. The proposed VM features a thread-block-level programming model, a hierarchical memory space, a novel algebraic layout system, and extensive support for diverse low-precision data types. VM programs are compiled into highly efficient GPU programs with automatic vectorization and instruction selection. Extensive experiments demonstrate that our VM efficiently supports a full spectrum of low-precision data types, and outperforms state-of-the-art low-precision kernels on their supported types. Compared to existing compilers like Triton and Ladder, as well as hand-optimized kernels such as QuantLLM and Marlin, our VM achieves performance improvements of 1.75x, 2.61x, 1.29x and 1.03x, respectively.

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