How does vLLM optimize GPU memory usage during inference

All answers to this question.

The core innovation driving this framework's extreme memory efficiency is PagedAttention, which is directly inspired by virtual memory management concepts in classic operating systems. In traditional serving architectures, memory for the key-value cache of a request had to be allocated contiguously in advance to account for the maximum possible sequence length. This design created massive internal fragmentation and wasted up to sixty percent of actual memory space on ungenerated tokens. This open-source engine eliminates this issue completely by dividing the cache into compact, fixed-sized blocks that can be scattered across non-contiguous spaces. It dynamically maps logical tokens to physical pages as the generation progresses, unlocking unprecedented concurrency thresholds.

   Answered 2025-05-19 by Pamela Thorne

Have you monitored your specific internal fragmentation metrics during peak hours to see if your current system is dropping active requests due to memory overallocation before the context limits are actually reached?

   Answered 2025-05-22 by Jeffrey Briggs

• Yes, we observed that our older serving system was crashing frequently under sustained loads even when the actual token count was quite low. The system was pre-allocating large memory chunks for the maximum possible generation length for every single user thread, which exhausted our physical VRAM prematurely. This forced us to look into dynamic allocation methods to keep our services online.

     Commented 2025-05-25 by Melissa Vance

By storing the key-value cache non-contiguously, you can also enable safe memory sharing between completely different user prompts that share identical system instruction prefixes.

   Answered 2025-05-28 by Gregory Vance

• That prefix sharing feature is a massive game-changer for customer service bots. When thousands of unique users query an agent that uses a massive system prompt, the engine only keeps a single physical copy of that prefix cache in memory, saving an immense amount of enterprise cluster resources.

     Commented 2025-05-30 by Pamela Thorne