Demystifying AI Platform Design for Distributed Inference of Next-Generation LLM models

• URL: http://arxiv.org/abs/2406.01698v2
• Date: Tue, 29 Apr 2025 23:25:27 GMT
• Title: Demystifying AI Platform Design for Distributed Inference of Next-Generation LLM models
• Authors: Abhimanyu Bambhaniya, Ritik Raj, Geonhwa Jeong, Souvik Kundu, Sudarshan Srinivasan, Suvinay Subramanian, Midhilesh Elavazhagan, Madhu Kumar, Tushar Krishna,
• Abstract summary: Large language models (LLMs) have shown remarkable performance across a wide range of applications, often outperforming human experts.<n>With constant innovation in LLM serving optimizations and model architecture evolving at breakneck speed, the hardware requirements to meet Service Level Objectives (SLOs) remain an open research question.<n>We present an analytical tool, GenZ, to efficiently navigate the relationship between diverse LLM model architectures and AI platform design parameters.
• Score: 8.02264001053969
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large language models (LLMs) have shown remarkable performance across a wide range of applications, often outperforming human experts. However, deploying these gigantic models efficiently for diverse inference use cases requires carefully designed hardware platforms with ample computing, memory, and network resources. With constant innovation in LLM serving optimizations and model architecture evolving at breakneck speed, the hardware requirements to meet Service Level Objectives (SLOs) remain an open research question. To answer the question, we present an analytical tool, GenZ, to efficiently navigate the relationship between diverse LLM model architectures(Dense, GQA, MoE, Mamba), LLM serving optimizations(Chunking, Speculative decoding, quanitization), and AI platform design parameters. Our tool estimates LLM inference performance metrics for the given scenario. We have validated against real hardware platforms running various different LLM models, achieving a max geomean error of 5.82.We use GenZ to identify compute, memory capacity, memory bandwidth, network latency, and network bandwidth requirements across diverse LLM inference use cases. We also study diverse architectural choices in use today (inspired by LLM serving platforms from several vendors) to help inform computer architects designing next-generation AI hardware accelerators and platforms. The trends and insights derived from GenZ can guide AI engineers deploying LLMs as well as computer architects designing next-generation hardware accelerators and platforms. Ultimately, this work sheds light on the platform design considerations for unlocking the full potential of large language models across a spectrum of applications. The source code is available at https://github.com/abhibambhaniya/GenZ-LLM-Analyzer . Users can also be tried it on at https://genz-llm-analyzer.streamlit.app/ without any setup on your web browser.

Related papers

• Understanding and Optimizing Multi-Stage AI Inference Pipelines [11.254219071373319]
  HERMES is a Heterogeneous Multi-stage LLM inference Execution Simulator. HERMES supports heterogeneous clients executing multiple models concurrently unlike prior frameworks. We explore the impact of reasoning stages on end-to-end latency, optimal strategies for hybrid pipelines, and the architectural implications of remote KV cache retrieval.
  arXiv  Detail & Related papers  (2025-04-14T00:29:49Z)
• PLM: Efficient Peripheral Language Models Hardware-Co-Designed for Ubiquitous Computing [48.30406812516552]
  We introduce the PLM, a Peripheral Language Model, developed through a co-design process that jointly optimize model architecture and edge system constraints. PLM employs a Multi-head Latent Attention mechanism and employs the squared ReLU activation function to encourage sparsity, thereby reducing peak memory footprint. evaluation results demonstrate that PLM outperforms existing small language models trained on publicly available data.
  arXiv  Detail & Related papers  (2025-03-15T15:11:17Z)
• Low-Rank Adapters Meet Neural Architecture Search for LLM Compression [1.8434042562191815]
  The rapid expansion of Large Language Models (LLMs) has posed significant challenges regarding the computational resources required for fine-tuning and deployment. Recent advancements in low-rank adapters have demonstrated their efficacy in parameter-efficient fine-tuning (PEFT) of these models. This paper comprehensively discusses innovative approaches that synergize low-rank representations with Neural Architecture Search (NAS) techniques.
  arXiv  Detail & Related papers  (2025-01-23T02:14:08Z)
• SynerGen-VL: Towards Synergistic Image Understanding and Generation with Vision Experts and Token Folding [66.74446220401296]
  We propose SynerGen-VL, a simple yet powerful encoder-free MLLM capable of both image understanding and generation. We introduce the token folding mechanism and the vision-expert-based progressive alignment pretraining strategy, which effectively support high-resolution image understanding. Our code and models shall be released.
  arXiv  Detail & Related papers  (2024-12-12T18:59:26Z)
• LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators [1.1028525384019312]
  Large Language Models (LLMs) have propelled groundbreaking advancements across several domains and are commonly used for text generation applications. We introduce LLM-Inference-Bench, a comprehensive benchmarking suite to evaluate the hardware inference performance of LLMs. Our benchmarking results reveal the strengths and limitations of various models, hardware platforms, and inference frameworks.
  arXiv  Detail & Related papers  (2024-10-31T18:34:59Z)
• Mini-InternVL: A Flexible-Transfer Pocket Multimodal Model with 5% Parameters and 90% Performance [78.48606021719206]
  Mini-InternVL is a series of MLLMs with parameters ranging from 1B to 4B, which achieves 90% of the performance with only 5% of the parameters. We develop a unified adaptation framework for Mini-InternVL, which enables our models to transfer and outperform specialized models in downstream tasks.
  arXiv  Detail & Related papers  (2024-10-21T17:58:20Z)
• Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models [79.41139393080736]
  Large language models (LLMs) have rapidly advanced and demonstrated impressive capabilities. In-Context Learning (ICL) and. Efficient Fine-Tuning (PEFT) are currently two mainstream methods for augmenting. LLMs to downstream tasks. We propose Reference Trustable Decoding (RTD), a paradigm that allows models to quickly adapt to new tasks without fine-tuning.
  arXiv  Detail & Related papers  (2024-09-30T10:48:20Z)
• Archon: An Architecture Search Framework for Inference-Time Techniques [31.655124464284523]
  Archon is a framework for selecting, combining, and stacking layers of inference-time techniques. We evaluate Archon architectures across a range of instruction-following, reasoning, and coding benchmarks.
  arXiv  Detail & Related papers  (2024-09-23T17:53:42Z)
• Sketch: A Toolkit for Streamlining LLM Operations [51.33202045501429]
  Large language models (LLMs) have achieved remarkable success. The flexibility of their output format poses challenges in controlling and harnessing the model's outputs. We present Sketch, an innovative toolkit designed to streamline LLM operations across diverse fields.
  arXiv  Detail & Related papers  (2024-09-05T08:45:44Z)
• Performance Law of Large Language Models [58.32539851241063]
  Performance law can be used to guide the choice of LLM architecture and the effective allocation of computational resources. Performance law can be used to guide the choice of LLM architecture and the effective allocation of computational resources without extensive experiments.
  arXiv  Detail & Related papers  (2024-08-19T11:09:12Z)
• SoupLM: Model Integration in Large Language and Multi-Modal Models [51.12227693121004]
  Training large language models (LLMs) requires significant computing resources. Existing publicly available LLMs are typically pre-trained on diverse, privately curated datasets spanning various tasks.
  arXiv  Detail & Related papers  (2024-07-11T05:38:15Z)
• New Solutions on LLM Acceleration, Optimization, and Application [14.995654657013741]
  Large Language Models (LLMs) have become extremely potent instruments with exceptional capacities for comprehending and producing human-like text in a range of applications. However, the increasing size and complexity of LLMs present significant challenges in both training and deployment. We provide a review of recent advancements and research directions aimed at addressing these challenges.
  arXiv  Detail & Related papers  (2024-06-16T11:56:50Z)
• Evaluating the Generalization Ability of Quantized LLMs: Benchmark, Analysis, and Toolbox [46.39670209441478]
  Large language models (LLMs) have exhibited exciting progress in multiple scenarios. As an effective means to reduce memory footprint and inference cost, quantization also faces challenges in performance degradation at low bit-widths. This work provides a comprehensive benchmark suite for this research topic, including an evaluation system, detailed analyses, and a general toolbox.
  arXiv  Detail & Related papers  (2024-06-15T12:02:14Z)
• Large Language Models as Software Components: A Taxonomy for LLM-Integrated Applications [0.0]
  Large Language Models (LLMs) have become widely adopted recently. Research explores their use both as autonomous agents and as tools for software engineering. LLMs-integrated applications, on the other hand, are software systems that leverage an LLM to perform tasks that would otherwise be impossible or require significant coding effort. This study provides a taxonomy for LLM-integrated applications, offering a framework for analyzing and describing these systems.
  arXiv  Detail & Related papers  (2024-06-13T21:32:56Z)
• Do Large Language Model Understand Multi-Intent Spoken Language ? [5.494472119991781]
  This research signifies a considerable breakthrough in leveraging Large Language Models (LLMs) for multi-intent spoken language understanding (SLU) Our approach re-imagines the use of entity slots in multi-intent SLU applications. We introduce the concept of Sub-Intent Instruction (SII) to amplify the analysis and interpretation of complex, multi-intent communications.
  arXiv  Detail & Related papers  (2024-03-07T13:30:52Z)
• LLM Inference Unveiled: Survey and Roofline Model Insights [62.92811060490876]
  Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges. Our survey stands out from traditional literature reviews by not only summarizing the current state of research but also by introducing a framework based on roofline model. This framework identifies the bottlenecks when deploying LLMs on hardware devices and provides a clear understanding of practical problems.
  arXiv  Detail & Related papers  (2024-02-26T07:33:05Z)
• Knowledge Fusion of Large Language Models [73.28202188100646]
  This paper introduces the notion of knowledge fusion for large language models (LLMs) We externalize their collective knowledge and unique strengths, thereby elevating the capabilities of the target model beyond those of any individual source LLM. Our findings confirm that the fusion of LLMs can improve the performance of the target model across a range of capabilities such as reasoning, commonsense, and code generation.
  arXiv  Detail & Related papers  (2024-01-19T05:02:46Z)
• Video Understanding with Large Language Models: A Survey [97.29126722004949]
  Given the remarkable capabilities of large language models (LLMs) in language and multimodal tasks, this survey provides a detailed overview of recent advancements in video understanding. The emergent capabilities Vid-LLMs are surprisingly advanced, particularly their ability for open-ended multi-granularity reasoning. This survey presents a comprehensive study of the tasks, datasets, benchmarks, and evaluation methodologies for Vid-LLMs.
  arXiv  Detail & Related papers  (2023-12-29T01:56:17Z)

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.