Flash Attention Mechanics: How Tiled Attention Fits in SRAM

2026-06-26 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

FlashAttention, a kernel-level rewrite introduced by Dao et al. (2022), significantly optimizes the self-attention operation by avoiding the materialization of the full N×N attention score matrix. Standard attention for a 4096-token sequence requires storing a 1.0 GB FP16 matrix, leading to over 4 GB of HBM I/O. FlashAttention eliminates this matrix, fitting computation tiles within approximately 129 KB of per-SM SRAM on an A100 GPU. This optimization is I/O-bound, maintaining identical FLOPs while drastically reducing HBM traffic by about 33× at 4K tokens and 129× at 16K tokens. The total attention memory footprint also sees a substantial reduction, dropping approximately 9× at 4K tokens.

Key takeaway

For Machine Learning Engineers optimizing large language models with long sequences, you should consider integrating FlashAttention. This kernel-level rewrite drastically reduces HBM traffic by 33× at 4K tokens and lowers total attention memory by 9×. Implementing FlashAttention can significantly improve model training and inference efficiency, especially on memory-constrained hardware like A100 GPUs, without increasing FLOPs.

Key insights

FlashAttention optimizes self-attention by avoiding full matrix materialization, fitting computations in SRAM to reduce HBM traffic.

Principles

• N×N attention matrices dominate memory and bandwidth for long sequences.
• I/O-bound operations benefit significantly from on-chip memory utilization.

Method

FlashAttention employs a kernel-level rewrite to process attention in tiles that fit within SRAM, eliminating the need to write the full attention score matrix to HBM.

In practice

• Reduce HBM traffic for attention by 33× at 4K tokens.
• Achieve ~9× total attention memory reduction at 4K tokens.
• Utilize ~129 KB per-SM SRAM for attention computations.

Topics

• FlashAttention
• Self-Attention
• GPU Optimization
• SRAM
• HBM Traffic Reduction
• Kernel Rewrites

Best for: AI Engineer, NLP Engineer, Research Scientist, Machine Learning Engineer, AI Scientist

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• How Attention Got So Efficient [GQA/MLA/DSA] — Efficient attention mechanisms like DSA significantly reduce LLM compute costs and memory while preserving performance.
• 13+ Attention Mechanisms You Should Know — Attention mechanisms enable AI models to dynamically focus on relevant tokens, crucial for understanding sequence context and meaning.
• FlashAttention-4: Supercharging Transformer Attention on NVIDIA Blackwell GPUs — FlashAttention-4 optimizes Transformer attention for NVIDIA Blackwell GPUs by co-designing algorithms and kernels to overcome asymmetric hardware scaling.
• Engram: How LLMs Finally Get Scalable Memory — Engram enhances LLM factual recall and reasoning by integrating a scalable, hash-based memory lookup system for direct knowledge retrieval.
• Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling — CQS Divide enables exact self-attention decomposition for memory-adaptive, OOM-free LLM processing.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.