arXiv:2606.15555v1 Announce Type: cross Abstract: In large language model (LLM) serving, each request accumulates persistent graphics processing unit (GPU) memory during service as its key-value cache grows with every generated token. Under high concurrency, aggregate memory usage therefore increases endogenously over time: the service process itself creates future capacity pressure. When memory capacity is exceeded, systems evict active requests, discarding cached state and restarting them later, which wastes computation and reduces throughput. We develop a discrete-time dynamical model of me
The rapid scaling of LLMs into commercial deployment is exposing fundamental hardware and software bottlenecks in their operational serving and capacity planning.
Efficient serving of LLMs is critical for their economic viability and widespread adoption, impacting both service providers and end-users due to performance and cost implications.
The focus is shifting from pure model development to optimizing the operational stack for LLM inference, particularly concerning memory management and throughput under concurrent loads.
- · GPU manufacturers
- · Cloud service providers
- · Software developers specializing in LLM serving optimization
- · Companies with proprietary LLM serving infrastructure
- · LLM providers with inefficient serving architectures
- · Users experiencing high latency or cost for LLM access
Increased research and development into novel memory management and scheduling techniques for LLM inference.
New hardware designs and software paradigms specifically tailored to alleviate memory bottlenecks in AI accelerators.
Potential consolidation or emergence of specialized companies offering highly optimized LLM serving solutions as a core service.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI