Enterprise LLM deployment faces a critical scalability challenge: organizations must optimize models systematically to scale AI initiatives within constrained compute budgets, yet the specialized expertise required for manual optimization remains a niche and scarce skillset. This challenge is particularly evident in managing GPU utilization across heterogeneous infrastructure while enabling teams with diverse workloads and limited LLM optimization experience to deploy models efficiently. We present OptiKIT, a distributed LLM optimization framework that democratizes model compression and tuning by automating complex optimization workflows for non-expert teams. OptiKIT provides dynamic resource allocation, staged pipeline execution with automatic cleanup, and seamless enterprise integration. In production, it delivers more than 2x GPU throughput improvement while empowering application teams to achieve consistent performance improvements without deep LLM optimization expertise. We share both the platform design and key engineering insights into resource allocation algorithms, pipeline orchestration, and integration patterns that enable large-scale, production-grade democratization of model optimization. Finally, we open-source the system to enable external contributions and broader reproducibility.

翻译：企业级大语言模型部署面临一个关键的可扩展性挑战：组织必须在有限的计算预算内系统性地优化模型以扩展人工智能计划，然而手动优化所需的专业知识仍属于稀缺的专项技能。这一挑战在管理异构基础设施的GPU利用率时尤为明显，同时需要让具有多样化工作负载且大语言模型优化经验有限的团队能够高效部署模型。本文提出OptiKIT——一个分布式大语言模型优化框架，通过为非专业团队自动化复杂的优化工作流，实现了模型压缩与调优的普及化。OptiKIT提供动态资源分配、具备自动清理功能的分阶段流水线执行以及无缝的企业集成能力。在生产环境中，该系统实现了超过2倍的GPU吞吐量提升，同时赋能应用团队在无需深厚大语言模型优化专业知识的情况下获得持续的性能改进。我们分享了平台设计的关键工程洞见，包括实现大规模生产级模型优化普及化的资源分配算法、流水线编排与集成模式。最后，我们将系统开源以促进外部贡献与更广泛的可复现性。