Service-Induced Congestion in Memory-Constrained LLM Serving

2026-06-16 · Source: stat.ML updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Software Development & Engineering · Depth: Expert, extended

Summary

A new study identifies "service-induced congestion" in large language model (LLM) serving, where persistent GPU memory accumulation from key-value (KV) caches during autoregressive decoding leads to endogenous capacity pressure. Under high concurrency, exceeding memory capacity forces active request eviction, wasting computation and reducing throughput. The research develops a discrete-time dynamical model, revealing that for homogeneous workloads, the eviction-free equilibrium is unstable, converging to a worst-case limit cycle with up to 50% throughput loss (when decoding lengths are large relative to input lengths). For heterogeneous workloads, stability depends on decoding length coprimality; coprime lengths stabilize the system, while non-coprime lengths cause synchronized instability. The work proposes rate-limited admission and request mixing as scheduling design principles, validated by model-based, Vidur, and real-GPU simulations.

Key takeaway

For MLOps Engineers optimizing LLM serving, recognize that continuous KV cache growth creates a unique, dynamic memory constraint. Your admission policies must anticipate future memory pressure, not just instantaneous fit. Avoid homogeneous workloads where possible, as they are structurally unstable and can lead to 50% throughput loss. Instead, prioritize mixing heterogeneous requests with coprime decoding lengths to desynchronize memory release, or implement rate-limited admission to prevent eviction cascades.

Key insights

LLM KV cache growth creates service-induced congestion, with stability determined by workload homogeneity and decoding length coprimality.

Principles

• LLM requests progressively consume GPU memory, unlike stateless inference.
• Homogeneous LLM workloads are prone to synchronized memory growth and throughput collapse.
• Coprime decoding lengths desynchronize memory release, stabilizing heterogeneous LLM systems.

Method

A discrete-time dynamical model captures LLM admission, KV cache growth, and eviction under continuous batching, analyzed via linear recurrence and spectral theory.

In practice

• Implement rate-limited admission to regulate concurrency and prevent memory overflow.
• Mix heterogeneous requests with coprime decoding lengths to desynchronize completions.
• Prioritize retaining later-stage requests during eviction (Least-Progressed-First rule).

Topics

• LLM Serving
• GPU Memory Management
• Continuous Batching
• Dynamical Systems
• Admission Control
• Workload Heterogeneity
• KV Cache

Best for: Research Scientist, AI Architect, AI Engineer, AI Scientist, Machine Learning Engineer, MLOps Engineer

Related on AIssential

• Service-Induced Congestion in Memory-Constrained LLM Serving — LLM serving's memory growth causes "service-induced congestion," leading to instability and significant throughput loss, which heterogeneity can mitigate.
• KV Cache Explained Like You’re an LLM Engineer — KV cache makes LLM inference viable by storing past Key/Value tensors, avoiding redundant attention recomputation.
• Sandwich: Joint Configuration Search and Hot-Switching for Efficient CPU LLM Serving — Optimizing CPU LLM serving requires separate execution plans for prefill and decode phases due to their distinct computational and memory demands.
• Serving Multiple Users at Once: How Continuous Batching Keeps LLM Inference Efficient — Continuous batching optimizes LLM inference by dynamically scheduling requests and packing tokens to eliminate padding and idle GPU time.
• An Interpretable Latency Model for Speculative Decoding in LLM Serving — A new latency model explains speculative decoding performance in LLM serving under dynamic loads.
• Tangram: Unlocking Non-Uniform KV Cache for Efficient Multi-turn LLM Serving — Tangram enables efficient multi-turn LLM serving by making non-uniform KV caches practical, boosting throughput by 2.6x.

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.