PagedAttention and KV cache management in LLM serving
An article published on September 24, 2026 presents PagedAttention, a virtual-memory-inspired approach to reducing KV cache fragmentation and duplication, which can limit batch size.
Source: PagedAttention gerencia a memória do KV cache em LLM serving (arxiv.org). Text prepared with AI from this source.
What happened and what to do
The article describes PagedAttention, an attention algorithm inspired by virtual memory that manages the KV cache during language-model serving. It addresses memory fragmentation and redundant duplication, issues that can constrain batch size. The article also connects memory management with understanding serving limits.
A company can assess its inference infrastructure, measure KV cache usage and fragmentation, and test allocation and serving strategies against representative workloads. Based on the findings, it can tune configurations, monitor capacity, and define indicators for tracking performance and resource consumption, without assuming gains before validation.
How the consultancy can help
Wendelmaques can diagnose memory usage and serving capacity limits, define a testing scope, and implement infrastructure monitoring and adjustments. Ongoing work can include tracking indicators and refining configurations as client needs evolve.
Next step
Send a brief description of your serving environment and the capacity issue you want to investigate to receive a scoped proposal.
Let's assess your project
I reply with scope and a proposal.