Scalable Joint Resource Allocation for SLO-Constrained LLM Inference in Heterogeneous GPU Clouds
arXiv:2604.07472v2 Announce Type: replace Abstract: Serving large language model (LLM) inference in cloud environments requires jointly optimizing model selection, GPU provisioning, parallelism configuration, and workload routing under latency, accuracy, memory, and budget constraints. While mixed-integer linear programming (MILP) can model this problem, its computational cost limits frequent re-optimization under demand variability. Existing heuristics often optimize individual components separately and may become infeasible when system-wide constraints are enforced. This paper presents a sca
The proliferation of complex LLMs and increased demand for their inference in cloud environments necessitates more efficient resource allocation solutions.
Optimizing LLM inference resource allocation is crucial for managing costs, improving performance, and scaling AI services, directly impacting the economic viability and accessibility of advanced AI.
This research introduces methods to overcome the computational and feasibility limitations of current resource allocation strategies for LLMs, enabling more adaptive and efficient cloud operations.
- · Cloud providers
- · LLM developers
- · AI-reliant businesses
- · GPU manufacturers
- · Inefficient cloud resource management practices
- · Companies with high LLM inference costs
More efficient and cost-effective deployment of large language models in cloud infrastructure.
Accelerated adoption of advanced AI capabilities across various industries due to reduced operational friction.
Increased global competition in AI services as barriers to large-scale LLM deployment are lowered, potentially shifting market leadership.
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Read at arXiv cs.LG