arXiv:2606.17081v1 Announce Type: cross Abstract: Disaggregated inference architectures physically separate prefill and decode phases onto distinct GPU pools, creating competing "agents" that share a fixed hardware budget. We provide, to our knowledge, the first formal game-theoretic analysis of this architecture, using NVIDIA Dynamo as a concrete case study. We model disaggregated serving as three coupled games: a two-player resource game between prefill and decode pools, a selfish caching game over the hierarchical KV cache, and a congestion game with positive externalities for request routi
The increasing complexity and resource demands of large language models necessitate more efficient and disaggregated inference architectures, prompting formal analysis to optimize their performance and cost.
Understanding the game-theoretic dynamics of disaggregated inference is crucial for designing scalable, cost-effective, and performant AI systems, impacting the underlying infrastructure of future AI applications.
This research provides a formal framework for optimizing resource allocation in advanced AI inference systems, moving from empirical adjustments to theoretically grounded design principles.
- · AI infrastructure providers
- · Hyperscalers
- · Organizations deploying large language models
- · GPU manufacturers
- · Legacy AI inference systems
- · Inefficient resource allocators
More efficient and cost-effective deployment of demanding AI models becomes possible.
This efficiency could accelerate the adoption and commercial viability of large AI models across various industries.
Optimized disaggregated architectures may reduce the overall compute footprint for inference, potentially impacting energy consumption trends in AI.
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Read at arXiv cs.AI