InfraMLForge: Developer Tooling for Rapid LLM Development and Scalable Deployment

InfraMLForge: Developer Tooling for Rapid LLM Development and Scalable Deployment | 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 InfraMLForge: Developer Tooling for Rapid LLM Development and Scalable Deployment Yuhan Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6793970/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 Large language models (LLMs) have transformed the landscape of artificial intelligence, presenting immense potential across various applications. However, the complexity involved in developing and deploying these models can hinder progress for many practitioners. To address this challenge, we introduce InfraMLForge, a comprehensive ecosystem that serves as a toolkit for developers. InfraMLForge is tailored to streamline the development process, allowing machine learning practitioners to prioritize innovation while alleviating infrastructure burdens. The toolkit features essential components such as model training utilities, dataset management capabilities, and versatile deployment pipelines optimized for cloud and edge environments. This integration facilitates smooth transitions throughout the development lifecycle and ensures thorough monitoring of models post-deployment. Through various case studies, we illustrate the operational effectiveness of InfraMLForge and its capacity to enhance developer productivity while supporting a range of workloads, effectively positioning it as a vital resource for advancing LLM applications. Computer Architecture and Engineering Scalable Deployment LLM Agents Tooling Development 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-6793970","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":464708288,"identity":"27820285-97c2-4682-a1d1-afd631468ebc","order_by":0,"name":"Yuhan Zhang","email":"data:image/png;base64,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","orcid":"","institution":"Northeastern University","correspondingAuthor":true,"prefix":"","firstName":"Yuhan","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-06-01 05:56:42","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-6793970/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6793970/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83803952,"identity":"ee89c2ee-6cd0-4ae9-a8d4-377a2f791942","added_by":"auto","created_at":"2025-06-03 04:12:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":864065,"visible":true,"origin":"","legend":"","description":"","filename":"InfraMLForgeDeveloperToolingforRapidLLMDevelopmentandScalableDeployment4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6793970/v1_covered_1c37d3f1-23e0-443f-bb7b-2cee42b03b58.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eInfraMLForge: Developer Tooling for Rapid LLM Development and Scalable Deployment\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Northeastern University","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":"Scalable Deployment, LLM Agents, Tooling Development","lastPublishedDoi":"10.21203/rs.3.rs-6793970/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6793970/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLarge language models (LLMs) have transformed the landscape of artificial intelligence, presenting immense potential across various applications. 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