FlashAttention-4: Supercharging Transformer Attention on NVIDIA Blackwell GPUs

2026-03-11 · Source: Artificial Intelligence in Plain English - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, quick

Summary

FlashAttention-4 significantly accelerates the attention layer of Transformer models on NVIDIA Blackwell GPUs, such as the B200 and GB200. This breakthrough addresses the asymmetric scaling of Blackwell hardware, where tensor cores became much faster but supporting units like memory and exponentiation did not. The FlashAttention-4 team co-designed algorithms and GPU kernels to overlap tasks, including data loading, matrix multiplications, and softmax computations, preventing idle units. It also employs software optimizations like polynomial approximations for exponentials and "on-the-fly" normalization to bypass slow hardware steps. This approach yields up to 2.7x speedup over Triton and 1.3x over NVIDIA's cuDNN on Blackwell, achieving approximately 1600 TFLOPs/s, which is 71% of peak performance. FlashAttention-4 is implemented using NVIDIA's CuTe-DSL, a Python-friendly CUDA kernel DSL, ensuring rapid compile times for developers.

Key takeaway

For Machine Learning Engineers optimizing Transformer model inference on NVIDIA Blackwell GPUs, FlashAttention-4 offers substantial performance gains. Your existing models can achieve up to 2.7x faster attention processing compared to Triton and 1.3x over cuDNN by integrating this solution. Consider adopting FlashAttention-4 to maximize throughput and efficiency on the latest NVIDIA hardware, leveraging its algorithmic and kernel co-design for improved performance.

Key insights

FlashAttention-4 optimizes Transformer attention for NVIDIA Blackwell GPUs by co-designing algorithms and kernels to overcome asymmetric hardware scaling.

Principles

• Overlap tasks to prevent unit idleness
• Use software approximations for slow hardware
• Co-design algorithms and hardware kernels

Method

FlashAttention-4 uses algorithm-kernel co-design to overlap data loading, matrix multiplication, and softmax, alongside polynomial approximations for exponentials and "on-the-fly" normalization to optimize Transformer attention on Blackwell GPUs.

In practice

• Achieve 2.7x speedup over Triton
• Attain 1.3x speedup over cuDNN
• Utilize CuTe-DSL for fast compilation

Topics

• FlashAttention-4
• NVIDIA Blackwell GPUs
• Transformer Attention
• GPU Kernel Optimization
• CUDA Kernel DSL

Best for: Machine Learning Engineer, NLP Engineer, AI Scientist, AI Researcher, AI Engineer, Deep Learning Engineer

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• Qwen Team Releases FlashQLA: a High-Performance Linear Attention Kernel Library That Achieves Up to 3× Speedup — FlashQLA significantly accelerates linear attention for Qwen models via specialized kernel optimizations.
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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence in Plain English - Medium.