OptiKIT自动化优化企业大模型，2倍提升显存效率
Meeting SLOs, Slashing Hours: Automated Enterprise LLM Optimization with OptiKIT
- 自动调度资源并分阶段执行优化流程
- 生产环境实现超过2倍的GPU吞吐量提升
- 让无经验团队也能高效部署大模型
企业大模型部署面临关键可扩展性挑战：必须在有限算力预算内系统性优化模型，但手动优化所需的专精技能稀缺。这一难题在异构基础设施上尤为突出，且团队负载多样、缺乏大模型优化经验。我们提出OPTIKIT，一个分布式大模型优化框架，通过自动化复杂优化流程，使非专家团队也能实现模型压缩与调优。该框架支持动态资源分配、分阶段流水线执行及自动清理，并无缝集成至企业环境。实际部署中，其带来超2倍的GPU吞吐量提升，帮助应用团队在无需深度优化知识的前提下持续获得性能改善。本文还分享了平台设计与关键工程实践，涵盖资源管理、流水线编排与集成模式，推动大规模、生产级的模型优化民主化。最后，系统已开源，以支持外部贡献与可复现性。
原文摘要 · Abstract (English)
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 management, 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.
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