Comparison
FlashAttention vs GPU
FlashAttention and GPU are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for FlashAttention
FlashAttention comes up when the question is fundamentally about architecture.
Training a 70B model on 8K context that would not fit with standard attention.
When you would reach for GPU
GPU comes up when the question is fundamentally about infrastructure.
NVIDIA H100: ~2 TB/s memory bandwidth, ~989 TF/s BF16.
Frequently asked
What is the difference between FlashAttention and GPU?
FlashAttention: FlashAttention is an algorithm that computes exact attention faster and with much less memory by carefully tiling the computation to fit in GPU SRAM rather than going to HBM. GPU: GPUs are the parallel processors that train and run nearly every modern AI model. Their throughput on matrix multiplication is what makes deep learning practical.
When should I use FlashAttention vs GPU?
FlashAttention is the right concept when you are focused on architecture. GPU applies when you are focused on infrastructure.
Are FlashAttention and GPU the same thing?
No. FlashAttention is architecture; GPU is infrastructure. They are related but address different parts of the AI stack.