Comparison
KV Cache vs vLLM
KV Cache and vLLM are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
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.
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 KV Cache and vLLM?
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. 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 KV Cache vs vLLM?
KV Cache is the right concept when you are focused on architecture. vLLM applies when you are focused on infrastructure.
Are KV Cache and vLLM the same thing?
No. KV Cache is architecture; vLLM is infrastructure. They are related but address different parts of the AI stack.