arXiv:2607.02043v1 Announce Type: cross Abstract: Disaggregated LLM serving runs prefill and decode on separate GPU pools to keep the two phases from interfering. In practice, this creates a new asymmetry: under bursty, heavy-tailed workloads prefill nodes saturate while decode nodes have compute underutilized, and on a production-style A100 cluster with 2 prefill and 2 decode nodes (2P2D), we find that prefill execution accounts for only 2-23% of P95 Time-to-First-Token (TTFT). Queuing and inter-node GPU-GPU KV-cache transfer account for the rest. We present a proactive prefill-deflecting sch
The increasing scale and complexity of LLMs, coupled with their deployment in real-world scenarios, highlights bottlenecks in current serving architectures.
Optimizing LLM serving infrastructure directly impacts the cost, latency, and scalability of AI applications, driving the practical adoption of large models.
This research suggests a more efficient approach to managing LLM prefill and decode phases, potentially improving overall system throughput and reducing underutilization of compute resources.
- · AI infrastructure providers
- · Cloud service providers
- · Companies deploying large LLMs
- · GPU manufacturers
- · Inefficient LLM serving solutions
- · Cloud users paying for underutilized compute
Improved performance and reduced operational costs for AI products and services.
Faster innovation cycles for LLM-based applications due to more efficient deployment.
Increased accessibility and broader adoption of advanced AI capabilities across industries.
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Read at arXiv cs.AI