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AgentCompile demonstrates how Large Language Models can be integrated into the compiler stack to generate optimized CUDA kernels, bridging high-level AI reasoning with low-performance hardware execution. By combining LLM-guided metadata generation with template-based validation and direct kernel synthesis for critical inference paths, this system achieves significant latency reductions over standard eager frameworks like PyTorch and vLLM without sacrificing correctness. This work highlights a promising trajectory where machine learning directly optimizes the underlying compute infrastructure rather than just serving as an application layer model.
AgentCompile: An LLM-Guided Compiler for Direct CUDA Inference
arXiv:2606.07665v2 Announce Type: replace-cross Abstract: 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 LLM directly generates five classes of decode-critical kernels to accelerate inference, prompted by distilled optimization principles. \textsc{AgentCompile} integrates these kernels into a serving runtime with paged KV cache, continuous batching, preemption, chunked prefill, and bucketed full-step CUDA Graph replay. Across six evaluated model families, \textsc{AgentCompile} achieves speedups of \textbf{2.23--6.98$\times$} over PyTorch eager for single-request generation, and \textbf{1.04--1.16$\times$} over vLLM for both single-request generation and multi-request serving. Our code is publicly available at https://github.com/veneno1213822/AgentCompile.
Abstract: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 LLM directly generates five classes of decode-critical kernels to accelerate inference, prompted by distilled optimization principles. \textsc{AgentCompile} integrates these kernels into a serving runtime with paged KV cache, continuous batching, preemption, chunked prefill, and bucketed full-step CUDA Graph replay. Across six evaluated model families, \textsc{AgentCompile} achieves speedups of \textbf{2.23--6.98$\times$} over PyTorch eager for single-request generation, and \textbf{1.04--1.16$\times$} over vLLM for both single-request generation and multi-request serving. Our code is publicly available at this https URL.
Programming Languages (cs.PL); Artificial Intelligence (cs.AI)
arXiv:2606.07665 [cs.PL]
(or arXiv:2606.07665v2 [cs.PL] for this version)
https://doi.org/10.48550/arXiv.2606.07665 Focus to learn more arXiv-issued DOI via DataCite
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