arXiv subjects

Ahasan Kabir

Publications and source records attributed to Ahasan Kabir.

2 recordsLinked to original sources

arXiv2026

HW-Router: Hardware-Aware Routing for Scalable Multi-LLM Serving

Modern large language model (LLM) serving platforms deploy multiple models across different GPUs, requiring routers to direct incoming queries to appropriate LLMs. However, existing routing approaches primarily rely on static model attributes such as size or FLOPs to estimate serving costs. This static cost modeling fails to capture the dynamic behavior of real deployments, where the same model can exhibit vastly different inference latencies depending on hardware type (e.g., H100 vs. V100), current system load (e.g., running and waiting queue lengths), and resource contention (e.g., KV-cache usage and GPU utilization). Such hardware-agnostic routing leads to suboptimal decisions, resulting in SLO violations, queue buildup, and underutilized GPUs. To address these challenges, we present HW-Router, a dynamic routing framework that integrates real-time hardware signals into model selection to enable accurate latency prediction and intelligent, SLO-aware routing decisions. Our approach incorporates model-specific features (architecture, size, input length) alongside hardware metrics including queue lengths, KV-cache utilization, and recent TTFT/TPOT performance, and uses a lightweight latency predictor to estimate per-model-per-GPU serving time. Evaluations across diverse workloads show that HW-Router achieves 3.4-3.9x lower end-to-end latency, 46-48 percentage points higher SLO attainment, 6-8x lower GPU load skew, and a 3.1-3.4x reduction in waiting-queue fraction compared to state-of-the-art router baselines, CARROT and IRT, with only ~200 us of additional routing overhead and no loss in output quality. These results highlight the importance of real-time hardware feedback for scalable, predictable, and well-balanced multi-LLM serving. Code is available at https://github.com/UCF-ML-Research/HW-Router.

cs.NI↗

arXiv2026

INFRAMIND: Infrastructure-Aware Multi-Agent Orchestration

Existing multi-agent LLM orchestration methods, ranging from brute-force ensembles to learned routers, select models and topologies based on task and model features. However, these methods do not consider the runtime state of the serving infrastructure. On shared GPU clusters under concurrent load, this infrastructure blindness causes systematic resource underutilization: preferred models accumulate deep request queues while equally capable alternatives sit idle. In multi-agent pipelines, where each query triggers multiple sequential model calls, these delays then compound across every downstream step. Closing this gap is challenging because the relevant infrastructure signals (queue depths, KV-cache pressure, latencies) are dynamic and noisy, and they must drive three different decisions: planning, per-step routing, and scheduling. We introduce INFRAMIND, a framework that makes the entire multi-agent stack infrastructure-aware. An infra-aware planner conditions topology and role selection on real-time system load and remaining budget, biasing toward simpler graphs under congestion and richer ones at low load. An infra-aware executor then observes per-model queue depths, cache utilization, and response latencies at each agent step to decide which model to call and how deeply to reason; a budget-aware scheduler further reorders each model's queue so that urgent requests are served first. Cast as a hierarchical constrained MDP and solved end-to-end via reinforcement learning, the system learns to balance quality against latency automatically. Across five benchmarks, INFRAMIND delivers up to +7.6 pp accuracy over the prior baseline at low load with up to 7x lower latency, and sustains up to 99.9% SLO compliance under high load where every baseline drops below 50%.

cs.AI↗