# Computation-Bandwidth-Memory Trade-offs: A Unified Paradigm for AI Infrastructure

arXiv: https://arxiv.org/abs/2601.11577 · HTML: https://mlsanity.com/paper/2601.11577/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2025.

## Built on by (most cited first, 3 of 3)

- [Enhancing Neural Video Compression of Static Scenes with Positive-Incentive Noise](https://mlsanity.com/paper/2603.06095/) (2026)
- [LinearKV: One Cached State Suffices for Position-Independent Caching in Hybrid LLMs](https://mlsanity.com/paper/2608.11231/) (2026)
- [Generative Transmission: Rethinking Computation, Bandwidth, and Memory in Communication](https://mlsanity.com/paper/2607.17482/) (2026)

## Abstract (arXiv)

Large-scale artificial intelligence models are transforming industries and redefining human machine collaboration. However, continued scaling exposes critical limitations in hardware, including constraints on computation, bandwidth, and memory. These dimensions are tightly interconnected, so improvements in one often create bottlenecks in others, making isolated optimizations less effective. Balancing them to maximize system efficiency remains a central challenge in scalable AI design. To address this challenge, we introduce Computation-Bandwidth-Memory Trade-offs, termed the AI Trinity, a unified paradigm that positions computation, bandwidth, and memory as coequal pillars for next-generation AI infrastructure. AI Trinity enables dynamic allocation of resources across these pillars, alleviating single-resource bottlenecks and adapting to diverse scenarios to optimize system performance. Within this framework, AI Trinity identifies three fundamental trade-offs: (1) More ComputationrightarrowLess Bandwidth, wherein computational resources are exploited to reduce data transmission under limited bandwidth conditions, (2) More BandwidthrightarrowLess Memory, which exploits abundant communication capacity to populate or refresh memory when local storage resources are constrained, and (3) More MemoryrightarrowLess Computation, whereby storage capacity are utilized to mitigate redundant computation when computational costs are prohibitive. We illustrate the effectiveness of AI Trinity through representative system designs spanning edge-cloud communication, large-scale distributed training, and model inference. The innovations embodied in AI Trinity advance a new paradigm for scalable AI infrastructure, providing both a conceptual foundation and practical guidance for a broad range of application scenarios.