Flash Attention vs Standard Attention: The GPU Memory Showdown

Every LLM inference and training run boils down to one bottleneck: the attention mechanism. Standard scaled dot-product attention materialises a full N×N matrix in HBM, while Flash Attention tiles it in SRAM. The performance difference is enormous — but the two approaches have very different trade-offs.

Standard Attention

The textbook formula:

Attention(Q, K, V) = softmax(QKᵀ / √d_k) · V

This requires materialising the full N×N attention matrix. For a 4096-token sequence with 32 heads, that’s ~2 GB of intermediate memory just for one layer.

Flash Attention v2

Flash Attention (Dao et al., 2022) tiles the computation into SRAM blocks, fuses the softmax and matmul, and never writes the full attention matrix to HBM. Version 2 adds:

Better work partitioning across GPU thread blocks
Support for causal masking without overhead
Backward pass optimizations via recomputation

Criteria

Flash Attention v2

Standard Attention

Memory (4K sequence)

~0.8 GB

~12 GB

Speed (A100 80GB)

3.2× faster

1.0× baseline

Long context (32K+)

Feasible

OOM on most GPUs

Implementation

CUDA kernel — complex

PyTorch one-liner — simple

Custom attention patterns

Limited (causal, sliding window)

Any pattern trivially

Numerical precision

Online softmax — slight diff

Exact reference

Framework support

PyTorch 2.0+, JAX, Triton

Everywhere

Multi-Query and Grouped-Query Attention

Modern LLMs (LLaMA 3, Gemma 2) use GQA — sharing key/value heads across query heads. This reduces KV cache size during inference. Flash Attention v2 supports GQA natively, making it even more essential for production serving.

When Standard Attention Still Wins

Prototyping: When you need a quick, debuggable implementation
Custom masking: Complex attention patterns (sparse, cross-attention with irregular shapes)
Short sequences: Under 512 tokens, the SRAM tiling overhead doesn’t pay off

Our Verdict

Winner Flash Attention v2

For any production workload with sequence lengths above 1K tokens, Flash Attention v2 is the unambiguous winner. The 3–4× speedup and 10–20× memory reduction make it essential for training and inference at scale. Standard attention remains useful only for prototyping and exotic masking patterns.