Scaling Intelligence: Designing Data Centers for Next-Gen Language Models

• URL: http://arxiv.org/abs/2506.15006v1
• Date: Tue, 17 Jun 2025 22:29:37 GMT
• Title: Scaling Intelligence: Designing Data Centers for Next-Gen Language Models
• Authors: Jesmin Jahan Tithi, Hanjiang Wu, Avishaii Abuhatzera, Fabrizio Petrini,
• Abstract summary: Large Language Models (LLMs) demand a radical rethinking of data center architecture to ensure scalability, efficiency, and cost-effectiveness.<n>Our work provides a comprehensive co-design framework that jointly explores FLOPS, bandwidth and capacity, multiple network topologies, and popular parallelism/optimization strategies.<n>Our findings offer actionable insights and a practical roadmap for designing AI data centers.
• Score: 0.13332839594069593
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The explosive growth of Large Language Models (LLMs) - such as GPT-4 with 1.8 trillion parameters - demands a radical rethinking of data center architecture to ensure scalability, efficiency, and cost-effectiveness. Our work provides a comprehensive co-design framework that jointly explores FLOPS, HBM bandwidth and capacity, multiple network topologies (two-tier vs. FullFlat optical), the size of the scale-out domain, and popular parallelism/optimization strategies used in LLMs. We introduce and evaluate FullFlat network architectures, which provide uniform high-bandwidth, low-latency connectivity between all nodes, and demonstrate their transformative impact on performance and scalability. Through detailed sensitivity analyses, we quantify the benefits of overlapping compute and communication, leveraging hardware-accelerated collectives, wider scale-out domains, and larger memory capacity. Our study spans both sparse (mixture of experts) and dense transformer-based LLMs, revealing how system design choices affect Model FLOPS Utilization (MFU = Model flops per token x Observed tokens per sec / Peak flops of the hardware) and overall throughput. For the co-design study, we extended and validated a performance modeling tool capable of predicting LLM runtime within 10% of real-world measurements. Our findings offer actionable insights and a practical roadmap for designing AI data centers that can efficiently support trillion-parameter models, reduce optimization complexity, and sustain the rapid evolution of AI capabilities.

Related papers

• Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies [66.83950068218033]
  Scaling Laws demonstrate that scaling model parameters and training data enhances learning performance.<n>Despite its potential to improve performance, the integration of scaling laws into deep reinforcement learning has not been fully realized.<n>This review addresses this gap by systematically analyzing scaling strategies in three dimensions: data, network, and training budget.
  arXiv  Detail & Related papers  (2025-08-05T08:03:12Z)
• Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning [39.73152182572741]
  This paper proposes a novel framework, named Quantized Split Federated Fine-Tuning Large AI Model (SFLAM)<n>By partitioning the training load between edge devices and servers, SFLAM can facilitate the operation of large models on devices.<n>SFLAM incorporates quantization management, power control, and bandwidth allocation strategies to enhance training efficiency.
  arXiv  Detail & Related papers  (2025-04-12T07:55:11Z)
• A Survey on Inference Optimization Techniques for Mixture of Experts Models [50.40325411764262]
  Large-scale Mixture of Experts (MoE) models offer enhanced model capacity and computational efficiency through conditional computation.<n> deploying and running inference on these models presents significant challenges in computational resources, latency, and energy efficiency.<n>This survey analyzes optimization techniques for MoE models across the entire system stack.
  arXiv  Detail & Related papers  (2024-12-18T14:11:15Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• Performance Modeling and Workload Analysis of Distributed Large Language Model Training and Inference [2.2231908139555734]
  We propose a general performance modeling methodology and workload analysis of distributed LLM training and inference. We validate our performance predictions with published data from literature and relevant industry vendors (e.g., NVIDIA)
  arXiv  Detail & Related papers  (2024-07-19T19:49:05Z)
• Efficient Heterogeneous Large Language Model Decoding with Model-Attention Disaggregation [15.35494431928751]
  Transformer-based large language models (LLMs) exhibit impressive performance in generative tasks but also introduce significant challenges in real-world serving.<n>We introduce model-attention disaggregation to enhance the efficiency of LLM decoding.<n>We develop and deploy Lamina, an LLM inference system that incorporates model-attention disaggregation in a distributed heterogeneous cluster.
  arXiv  Detail & Related papers  (2024-05-03T02:15:15Z)
• When Parameter-efficient Tuning Meets General-purpose Vision-language Models [65.19127815275307]
  PETAL revolutionizes the training process by requiring only 0.5% of the total parameters, achieved through a unique mode approximation technique. Our experiments reveal that PETAL not only outperforms current state-of-the-art methods in most scenarios but also surpasses full fine-tuning models in effectiveness.
  arXiv  Detail & Related papers  (2023-12-16T17:13:08Z)
• COMET: A Comprehensive Cluster Design Methodology for Distributed Deep Learning Training [42.514897110537596]
  Modern Deep Learning (DL) models have grown to sizes requiring massive clusters of specialized, high-end nodes to train. designing such clusters to maximize both performance and utilization--to amortize their steep cost--is a challenging task. We introduce COMET, a holistic cluster design methodology and workflow to jointly study the impact of parallelization strategies and key cluster resource provisioning on the performance of distributed DL training.
  arXiv  Detail & Related papers  (2022-11-30T00:32:37Z)
• FG-Net: Fast Large-Scale LiDAR Point CloudsUnderstanding Network Leveraging CorrelatedFeature Mining and Geometric-Aware Modelling [15.059508985699575]
  FG-Net is a general deep learning framework for large-scale point clouds understanding without voxelizations. We propose a deep convolutional neural network leveraging correlated feature mining and deformable convolution based geometric-aware modelling. Our approaches outperform state-of-the-art approaches in terms of accuracy and efficiency.
  arXiv  Detail & Related papers  (2020-12-17T08:20:09Z)
• Edge-assisted Democratized Learning Towards Federated Analytics [67.44078999945722]
  We show the hierarchical learning structure of the proposed edge-assisted democratized learning mechanism, namely Edge-DemLearn. We also validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions.
  arXiv  Detail & Related papers  (2020-12-01T11:46:03Z)
• Optimization-driven Machine Learning for Intelligent Reflecting Surfaces Assisted Wireless Networks [82.33619654835348]
  Intelligent surface (IRS) has been employed to reshape the wireless channels by controlling individual scattering elements' phase shifts. Due to the large size of scattering elements, the passive beamforming is typically challenged by the high computational complexity. In this article, we focus on machine learning (ML) approaches for performance in IRS-assisted wireless networks.
  arXiv  Detail & Related papers  (2020-08-29T08:39:43Z)
• On the Difficulty of Designing Processor Arrays for Deep Neural Networks [0.0]
  Camuy is a lightweight model of a weight-stationary systolic array for linear algebra operations. We present an analysis of popular models to illustrate how it can estimate required cycles, data movement costs, as well as systolic array utilization.
  arXiv  Detail & Related papers  (2020-06-24T19:24:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.