Comparison
Continuous Batching vs vLLM
Continuous Batching and vLLM 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 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 Continuous Batching and vLLM?
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. 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 Continuous Batching vs vLLM?
Continuous Batching is the right concept when you are focused on inference. vLLM applies when you are focused on infrastructure.
Are Continuous Batching and vLLM the same thing?
No. Continuous Batching is inference; vLLM is infrastructure. They are related but address different parts of the AI stack.