AgentCompile: An LLM-Guided Compiler for Direct CUDA Inference
Transformer inference increasingly relies on specialized compiler and runtime support, while recent LLMs can generate nontrivial CUDA kernels. However, unconstrained generation guarantees neither correctness nor performance. We present \textsc{AgentCompile}, an LLM-guided CUDA inference compiler that combines two complementary uses of LLMs. First, the LLM provides advisory metadata for compiler-derived region summaries and bounded candidate spaces. The compiler then instantiates template-based CUDA candidates, validates correctness, selects implementations by measured latency, and falls back when specialization is unsupported or unprofitable. Second, under compiler-defined contracts, the ...
| Authors | Xuanzhe Li, Ziyan Weng, Zhiyu Zhu et al. |
|---|---|
| Primary category | cs.PL |
| Published | 4 June 2026 |
| Last updated at source | 3 August 2026 |
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