Comparison
FlashAttention vs KV Cache
FlashAttention and KV Cache 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 KV Cache
KV Cache comes up when the question is fundamentally about architecture.
Generating a 4K-token response: the KV cache fills up to 4K entries per layer.
Frequently asked
What is the difference between FlashAttention and KV Cache?
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. KV Cache: The KV cache stores the key and value vectors of all earlier tokens during generation so the model does not recompute them at every step. It is the main memory cost of LLM inference.
When should I use FlashAttention vs KV Cache?
FlashAttention is the right concept when you are focused on architecture. KV Cache applies when you are focused on architecture.
Are FlashAttention and KV Cache the same thing?
No. FlashAttention is architecture; KV Cache is architecture. They are related but address different parts of the AI stack.