Comparison
Continuous Batching vs FlashAttention
Continuous Batching and FlashAttention are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Continuous Batching
Continuous Batching comes up when the question is fundamentally about inference.
A vLLM server: 200 concurrent users with variable-length responses; GPU utilization stays at 95% vs ~30% on static batching.
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.
Frequently asked
What is the difference between Continuous Batching and FlashAttention?
Continuous Batching: Continuous batching lets new requests join an in-flight batch on the next decode step rather than waiting for the current batch to finish, dramatically raising GPU utilization on variable-length workloads. 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.
When should I use Continuous Batching vs FlashAttention?
Continuous Batching is the right concept when you are focused on inference. FlashAttention applies when you are focused on architecture.
Are Continuous Batching and FlashAttention the same thing?
No. Continuous Batching is inference; FlashAttention is architecture. They are related but address different parts of the AI stack.