Triton Fused Ops: A High-Performance Fused Operator Library for Transformer Inference

This article introduces the open-source project AICL-Lab/triton-fused-ops, a fused operator library based on OpenAI Triton optimized specifically for Transformer inference. Key features include: deep optimization for core computation patterns like RMSNorm+RoPE, Gated MLP, and FP8 GEMM; a correctness-first approach (each kernel comes with a NumPy reference implementation for CPU validation); support for production-ready FP8 quantization; and provision of auto-tuning and benchmarking tools. The project balances performance with engineering rigor, making it a valuable reference for Transformer inference optimization and production deployment.

In large language model inference optimization, operator fusion is key to improving efficiency. Unlike libraries that 'only pursue speed', this project adopts correctness over performance as its core design philosophy: each fused operator is equipped with a NumPy reference implementation, allowing correctness validation in GPU-free environments, reducing debugging difficulty, and providing a reliable benchmark. Another feature is production-ready FP8 support, which implements a complete FP8 GEMM pipeline (including explicit scaling management and overflow handling), offering practical references for FP8 inference in production environments.

The project's API architecture is divided into four layers: Validation Layer (checks input compliance), Reference Computation Layer (NumPy implementation for CPU validation), Kernel Layer (Triton implementation for high-performance GPU computation), and Tool Layer (auto-tuning, benchmarking, etc.). Core operators include:

• RMSNorm+RoPE Fusion: Merges the two operations to reduce memory read/write. On A100, it achieves up to 3x speedup and 40% reduction in memory traffic, suitable for long-sequence scenarios.
• Gated MLP Fusion: Integrates operations like gated projection. On A100, it offers 1.3-1.8x speedup and 25% reduction in memory traffic, contributing significantly to overall latency reduction.
• FP8 GEMM: Supports FP8 on Hopper architecture, including scaling factor management and overflow detection. On A100, it provides 1.2-1.5x speedup and 50% reduction in weight memory usage, beneficial for memory-constrained environments.

The project includes built-in TritonAutoTuner and ConfigCache mechanisms: The auto-tuner searches for optimal compilation parameters based on hardware/input shapes, while ConfigCache persists results to avoid repeated tuning. The benchmarking methodology is rigorous: 10 warm-up runs to eliminate cold starts, 100 test runs to take the average, and CUDA synchronization for each run to ensure accurate timing—all to guarantee reproducible and comparable performance data.

The project uses OpenSpec-driven development (changes require writing design documents first) to improve code quality and maintainability. The development workflow includes: code style checks (ruff, mypy), CPU-only validation tests, full GPU benchmarking, and build & release. The documentation system is layered: Academy (for beginners), Architecture Lab (for technical interview preparation), Performance (tuning practices), and Reference & Research (resources for researchers)—catering to different user needs.

Performance Data (in NVIDIA A100 SXM4 80GB environment):

Kernel	Speedup vs. PyTorch	Memory Traffic Reduction
fused_rmsnorm_rope	Up to ~3.0x	~40%
fused_gated_mlp	1.3-1.8x	~25%
fp8_gemm	1.2-1.5x	~50% (weights)

Technical Dependencies: Based on OpenAI Triton (compiler and Python DSL), PyTorch (tensor runtime), and NVIDIA CUDA (low-level GPU computation) to ensure performance and portability.

Triton Fused Ops builds high-performance AI inference infrastructure with engineering rigor, providing verifiable correctness, reproducible benchmarks, and maintainable code—making it an important reference for Transformer inference optimization and production deployment. As large model sizes grow, inference efficiency becomes increasingly critical. Such open-source projects offer optimization experiences and reusable implementations to the community, driving the advancement of AI infrastructure.