Meeting SLOs, Slashing Hours: Automated Enterprise LLM Optimization with OptiKIT

2026-01-28

AI Summary

This work addresses the challenges enterprises face in deploying large language models (LLMs), where limited computational budgets and a lack of specialized optimization expertise often result in low GPU utilization and inefficient deployment. To democratize large-scale LLM optimization for non-expert teams, we propose OptiKIT, a novel framework that integrates dynamic resource scheduling, a staged optimization pipeline, automated cleanup mechanisms, and enterprise-grade system integration within a distributed architecture to automate model compression and tuning. In real-world production environments, OptiKIT substantially lowers the barrier to AI deployment, enabling application teams without deep optimization experience to reliably meet performance targets while achieving over a 2× improvement in GPU throughput.

Technology Category

• Machine Learning: Optimization
• Search and Optimization: Distributed Search
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• LLM optimization
• enterprise deployment
• GPU utilization
• scalability
• resource constraints

Innovation

• automated LLM optimization
• distributed optimization framework
• dynamic resource allocation
• model compression
• enterprise AI deployment