Optimizing Large Language Model Scaling with Micro Batch Pipeline and Inference Parallelism

Optimizing Large Language Model Scaling with Micro Batch Pipeline and Inference Parallelism | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Optimizing Large Language Model Scaling with Micro Batch Pipeline and Inference Parallelism Doudou Quan, Ruoxi Wang, Zhu Lian, Naixin Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4575587/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Natural language processing has seen transformative progress with the development of sophisticated models capable of generating and understanding human language with high accuracy. The novel concept of integrating micro batch pipeline and inference parallelism represents a significant leap in optimizing the scalability and efficiency of these models. Through comprehensive experimentation with a modified GPT-Neo, substantial improvements were achieved in throughput, latency, perplexity, and BLEU scores, highlighting the effectiveness of the proposed methodologies. The enhanced model demonstrated superior performance in processing large datasets, maintaining high accuracy and quality of outputs, thereby addressing critical bottlenecks in computational load and resource constraints. The study demonstrates the potential of advanced parallelism techniques in revolutionizing model training and deployment, contributing valuable insights into the future of natural language processing and artificial intelligence. Artificial Intelligence and Machine Learning Scalability Parallelism Micro Batch Inference NLP Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4575587","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":313994179,"identity":"5b3b5825-e0ec-41a0-833a-d3a4807b473d","order_by":0,"name":"Doudou Quan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABE0lEQVRIie3PsUrDQBjA8QuBdvlq1wvRe4YrgcOikldJCXRKQPAFOiWDye5jZJKMlWC6fBbcEgKOnRScxEIEj9pGlCSuDveH8B3hftwdISrVP8wm+lKOWn4OIa/Nf37aSSaLgdxK8h3RbuSAL0I7CV8ClyPbER0aQnpIhYJu04zxlZubF+kTs4fxffJ+Sck4vHZaydoTRoyZxXEzN328sgDW8zKSF6P4kLQSBEFHQTZLCk+YfuDMIuqJAiTh1O8go1vj40Cme1LWvQTezOYUbU+qvlMm0cA9Owlyy8CNO43RsQBRVMecQtdbbNAfy5egZkcr967Ypg4bhpEon+tzNg7jVvIzbfG9hr+3/yYqlUqlOvQJhxxl+icndh4AAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0004-1392-9385","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Doudou","middleName":"","lastName":"Quan","suffix":""},{"id":313994180,"identity":"734add3e-6a8a-4b16-bab2-a03654ad14e3","order_by":1,"name":"Ruoxi Wang","email":"","orcid":"https://orcid.org/0009-0002-2799-5602","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ruoxi","middleName":"","lastName":"Wang","suffix":""},{"id":313994181,"identity":"8e46581f-6553-4e78-80d3-7b2119a92e63","order_by":2,"name":"Zhu Lian","email":"","orcid":"https://orcid.org/0009-0009-8965-9197","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Zhu","middleName":"","lastName":"Lian","suffix":""},{"id":313994182,"identity":"62eaeb6b-8f72-4361-aa40-8c26a3df06d5","order_by":3,"name":"Naixin Wang","email":"","orcid":"https://orcid.org/0009-0009-7014-7239","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Naixin","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-06-13 10:43:16","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4575587/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4575587/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58328784,"identity":"a5412821-8e3d-4991-8585-7f51ba797fb4","added_by":"auto","created_at":"2024-06-14 03:07:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":185607,"visible":true,"origin":"","legend":"","description":"","filename":"L.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4575587/v1_covered_4ee1f355-8792-49dc-bfad-6ca13e651bee.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eOptimizing Large Language Model Scaling with Micro Batch Pipeline and Inference Parallelism\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Scalability, Parallelism, Micro Batch, Inference, NLP","lastPublishedDoi":"10.21203/rs.3.rs-4575587/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4575587/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNatural language processing has seen transformative progress with the development of sophisticated models capable of generating and understanding human language with high accuracy. 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