CuTeGen: An LLM-Based Agentic Framework for Generation and Optimization of High-Performance GPU Kernels using CuTe
arXiv:2604.01489v2 Announce Type: replace 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 over the CuTe abstraction layer. Two design choices distinguish CuTeGen from prior work: targeting CuTe ra
The increasing demand for high-performance AI inference and training, particularly for large language models, makes efficient GPU kernel development a critical bottleneck, driving innovation in automation.
This development represents a significant step towards automating highly specialized and labor-intensive aspects of AI hardware optimization, directly impacting the scalability and efficiency of AI development.
The process of generating and optimizing high-performance GPU kernels moves closer to full automation, potentially reducing development time and expertise requirements for advanced AI systems.
- · AI developers
- · GPU manufacturers
- · Cloud providers
- · Machine learning systems
- · Manual GPU kernel optimization specialists
More efficient and faster development of specialized AI models and applications.
Reduced operational costs for AI training and inference at scale due to optimized hardware utilization.
Accelerated innovation cycle in AI leading to new capabilities and a more competitive landscape for AI infrastructure.
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Read at arXiv cs.LG