What problem does PagedAttention solve, and what is continuous batching?

For MLOps Engineer AI / LLM Engineer ML Engineer

The short answer

PagedAttention stores the KV cache in non-contiguous fixed-size blocks like OS virtual-memory pages, eliminating the fragmentation and over-reservation of contiguous KV allocation and enabling sharing across sequences. Continuous batching dynamically adds and removes requests from a batch at the token level instead of waiting for the whole batch to finish, sharply improving GPU utilization and throughput.

How to think about it

PagedAttention stores the KV cache in non-contiguous fixed-size blocks like OS virtual-memory pages, eliminating the fragmentation and over-reservation of contiguous KV allocation and enabling sharing across sequences. Continuous batching dynamically adds and removes requests from a batch at the token level instead of waiting for the whole batch to finish, sharply improving GPU utilization and throughput.

Learn it properly KV cache & continuous batching

Keep practising

How do you optimise GPU utilization for model serving, and what role does dynamic batching play? What is a KV cache and how does it speed up LLM inference? What is gradient accumulation and why is it useful? What is sequence padding and why is it necessary for batch training? What is the KV cache in a transformer and why does it matter for inference?

All MLOps questions

Explore further

Batch vs real-time inference Self-attention Batch size ↔ learning rate

PagedAttention Continuous Batching KV Cache Batch Processing