CuTeGen: An LLM-Based Agentic Framework for Generation and Optimization of High-Performance GPU Kernels using CuTe

📰 ArXiv cs.AI

Learn how CuTeGen, an LLM-based framework, generates and optimizes high-performance GPU kernels, and how you can apply this to improve your own machine learning systems

advanced Published 5 Jun 2026

Action Steps

Implement CuTeGen framework using CuTe to generate GPU kernels
Use LLMs to automate kernel generation and optimization
Test and refine generated kernels using standardized benchmarks
Compare performance of generated kernels with carefully tuned references
Apply CuTeGen to real-world machine learning systems to improve performance

Who Needs to Know This

Machine learning engineers and researchers can benefit from CuTeGen to automate kernel generation and optimization, improving the performance of their systems

Key Insight

💡 CuTeGen treats kernel development as a structured generate-test-refine workflow, enabling automated generation and optimization of high-performance GPU kernels

Key Takeaways

Learn how CuTeGen, an LLM-based framework, generates and optimizes high-performance GPU kernels, and how you can apply this to improve your own machine learning systems

Full Article

Title: CuTeGen: An LLM-Based Agentic Framework for Generation and Optimization of High-Performance GPU Kernels using CuTe

Abstract:
arXiv:2604.01489v2 Announce Type: replace-cross Abstract: High-performance GPU kernels are critical to modern machine learning systems, yet developing them remains a manual, expert-driven process. Recent work has explored using LLMs to automate kernel generation, but generated kernels still fall short of carefully tuned references on standardized benchmarks. We present CuTeGen, an agentic GPU kernel synthesis framework that treats kernel development as a structured generate-test-refine workflow

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