# TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators

arXiv: https://arxiv.org/abs/2502.14752 · HTML: https://mlsanity.com/paper/2502.14752/

## Key facts

- Cited by 75 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2025.

## Built on by (most cited first, 30 of 75)

- [Kevin: Multi-Turn RL for Generating CUDA Kernels](https://mlsanity.com/paper/2507.11948/) (2025)
- [Astra: A Multi-Agent System for GPU Kernel Performance Optimization](https://mlsanity.com/paper/2509.07506/) (2025)
- [AutoTriton: Automatic Triton Programming with Reinforcement Learning in LLMs](https://mlsanity.com/paper/2507.05687/) (2025)
- [Geak: Introducing Triton Kernel AI Agent & Evaluation Benchmarks](https://mlsanity.com/paper/2507.23194/) (2025)
- [FlashInfer-Bench: Building the Virtuous Cycle for AI-driven LLM Systems](https://mlsanity.com/paper/2601.00227/) (2026)
- [TritonRL: Training LLMs to Think and Code Triton Without Cheating](https://mlsanity.com/paper/2510.17891/) (2025)
- [QiMeng-Kernel: Macro-Thinking Micro-Coding Paradigm for LLM-Based High-Performance GPU Kernel Generation](https://mlsanity.com/paper/2511.20100/) (2025)
- [SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits](https://mlsanity.com/paper/2603.19173/) (2026)
- [From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence](https://mlsanity.com/paper/2511.18538/) (2025)
- [ConCuR: Conciseness Makes State-of-the-Art Kernel Generation](https://mlsanity.com/paper/2510.07356/) (2025)
- [KernelFoundry: Hardware-aware evolutionary GPU kernel optimization](https://mlsanity.com/paper/2603.12440/) (2026)
- [KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels](https://mlsanity.com/paper/2605.04956/) (2026)
- [Towards Automated Kernel Generation in the Era of LLMs](https://mlsanity.com/paper/2601.15727/) (2026)
- [AI for Auto-Research: Roadmap & User Guide](https://mlsanity.com/paper/2605.18661/) (2026)
- [CUDABench: Benchmarking LLMs for Text-to-CUDA Generation](https://mlsanity.com/paper/2603.02236/) (2026)
- [AKG kernel Agent: A Multi-Agent Framework for Cross-Platform Kernel Synthesis](https://mlsanity.com/paper/2512.23424/) (2025)
- [StitchCUDA: An Automated Multi-Agents End-to-End GPU Programing Framework with Rubric-based Agentic Reinforcement Learning](https://mlsanity.com/paper/2603.02637/) (2026)
- [ISO-Bench: Can Coding Agents Optimize Real-World Inference Workloads?](https://mlsanity.com/paper/2602.19594/) (2026)
- [KernelCraft: Benchmarking for Agentic Close-to-Metal Kernel Generation on Emerging Hardware](https://mlsanity.com/paper/2603.08721/) (2026)
- [Making LLMs Optimize Multi-Scenario CUDA Kernels Like Experts](https://mlsanity.com/paper/2603.07169/) (2026)
- [InCoder-32B-Thinking: Industrial Code World Model for Thinking](https://mlsanity.com/paper/2604.03144/) (2026)
- [GPU Kernel Optimization Beyond Full Builds: An LLM Framework with Minimal Executable Programs](https://mlsanity.com/paper/2512.22147/) (2025)
- [A Two-Stage GPU Kernel Tuner Combining Semantic Refactoring and Search-Based Optimization](https://mlsanity.com/paper/2601.12698/) (2026)
- [DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels](https://mlsanity.com/paper/2602.11715/) (2026)
- [OptiML: An End-to-End Framework for Program Synthesis and CUDA Kernel Optimization](https://mlsanity.com/paper/2602.12305/) (2026)
- [Towards Cold-Start Drafting and Continual Refining: A Value-Driven Memory Approach with Application to NPU Kernel Synthesis](https://mlsanity.com/paper/2603.10846/) (2026)
- [DRTriton: Large-Scale Synthetic Data Driven Reinforcement Learning for Triton Kernel Generation](https://mlsanity.com/paper/2603.21465/) (2026)
- [AscendOptimizer: Episodic Agent for Ascend NPU Operator Optimization](https://mlsanity.com/paper/2603.23566/) (2026)
- [AdaExplore: Failure-Driven Adaptation and Diversity-Preserving Search for Efficient Kernel Generation](https://mlsanity.com/paper/2604.16625/) (2026)
- [FastKernels: Benchmarking GPU Kernel Generation in Production](https://mlsanity.com/paper/2605.23215/) (2026)

## Abstract (arXiv)

Triton, a high-level Python-like language designed for building efficient GPU kernels, is widely adopted in deep learning frameworks due to its portability, flexibility, and accessibility. However, programming and parallel optimization still require considerable trial and error from Triton developers. Despite advances in large language models (LLMs) for conventional code generation, these models struggle to generate accurate, performance-optimized Triton code, as they lack awareness of its specifications and the complexities of GPU programming. More critically, there is an urgent need for systematic evaluations tailored to Triton. In this work, we introduce TritonBench, the first comprehensive benchmark for Triton operator generation. TritonBench features two evaluation channels: a curated set of 184 real-world operators from GitHub and a collection of operators aligned with PyTorch interfaces. Unlike conventional code benchmarks prioritizing functional correctness, TritonBench also profiles efficiency performance on widely deployed GPUs aligned with industry applications. Our study reveals that current state-of-the-art code LLMs struggle to generate efficient Triton operators, highlighting a significant gap in high-performance code generation. TritonBench will be available at https://github.com/thunlp/TritonBench.