Comparison
FlashAttention vs vLLM
FlashAttention and vLLM 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 vLLM
vLLM comes up when the question is fundamentally about infrastructure.
Serving Llama 3 70B at high QPS on 4 H100s with vLLM.
Frequently asked
What is the difference between FlashAttention and vLLM?
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. vLLM: vLLM is an open-source high-throughput LLM serving engine. Its PagedAttention KV cache manager is the reason it dramatically outperforms naive serving setups.
When should I use FlashAttention vs vLLM?
FlashAttention is the right concept when you are focused on architecture. vLLM applies when you are focused on infrastructure.
Are FlashAttention and vLLM the same thing?
No. FlashAttention is architecture; vLLM is infrastructure. They are related but address different parts of the AI stack.