Service-Induced Congestion in Memory-Constrained LLM Serving

2026-06-14 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Expert, quick

Summary

Large language model (LLM) serving faces "service-induced congestion" where GPU memory accumulates due to growing key-value caches for each request, particularly under high concurrency. This endogenous memory usage often exceeds capacity, leading to request eviction, wasted computation, and reduced throughput. A discrete-time dynamical model of memory-constrained LLM inference, capturing admission, memory growth, and eviction under continuous batching, was developed. Findings show that in a saturated-input regime, homogeneous workloads exhibit an unstable eviction-free equilibrium, converging to a worst-case limit cycle with up to 50% throughput loss. For heterogeneous workloads, a stability criterion was proven, demonstrating that coprime decoding lengths stabilize the eviction-free equilibrium, while non-coprime lengths create unstable synchronized modes. This research characterizes how workload heterogeneity can desynchronize completions and stabilize memory-constrained serving, identifying service-induced congestion as a structural instability and deriving scheduling design principles.

Key takeaway

For MLOps Engineers managing LLM serving infrastructure, understanding service-induced congestion is critical. Your systems may experience up to 50% throughput loss with homogeneous workloads due to unstable memory usage. To mitigate this, consider implementing scheduling strategies that introduce workload heterogeneity. Specifically, prioritize request batching or routing that encourages coprime decoding lengths to stabilize memory-constrained serving and sustain high throughput. This approach can prevent costly request evictions and wasted computation.

Key insights

LLM serving's memory growth causes "service-induced congestion," leading to instability and significant throughput loss, which heterogeneity can mitigate.

Principles

• Service-induced congestion is a structural instability in memory-constrained LLM serving.
• Homogeneous LLM workloads can lead to unstable equilibria and significant throughput loss.
• Workload heterogeneity, specifically coprime decoding lengths, can stabilize LLM serving.

Method

A discrete-time dynamical model captures admission, memory growth, and eviction in memory-constrained LLM inference under continuous batching.

In practice

• Design LLM scheduling to account for service-induced memory growth.
• Introduce workload heterogeneity to stabilize memory-constrained LLM serving.
• Prioritize coprime decoding lengths for heterogeneous LLM requests.

Topics

• LLM Serving
• GPU Memory Management
• Service Congestion
• Continuous Batching
• Workload Heterogeneity
• Throughput Optimization

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.