Efficient LLM Serving for Agentic Workflows: A Data Systems Perspective
Helium framework boosts LLM workflow efficiency via proactive caching and scheduling, achieving up to 1.56x speedup.
Key Findings
Methodology
Helium models agentic workflows as query plans, leveraging proactive KV caching and cache-aware scheduling. It uses templated radix trees to identify shared structures and cost-based scheduling for optimization.
Key Results
- Helium achieves a 1.34x speedup in complex financial analysis workflows and up to 1.56x in primitive workflows.
- Compared to vLLM, Helium significantly reduces redundant computation and improves GPU utilization and KV cache reuse.
- Eliminates redundant subgraph computations, enhancing efficiency in multi-agent collaboration scenarios.
Significance
This study addresses inefficiencies in existing LLM serving systems by introducing database query optimization principles to agentic workflows, improving scalability and efficiency for complex tasks.
Technical Contribution
Introduced proactive KV caching and cache-aware scheduling algorithms, combined with templated radix trees for cross-workflow sharing optimization. First application of query optimization techniques to LLM serving.
Novelty
Helium uniquely treats LLM calls as first-class operators in query plans, proactively optimizing workflow structures, unlike existing passive caching strategies.
Limitations
- Currently supports only single base LLM, limiting multi-model collaboration scenarios.
- Optimization relies on static workflow structures, with limited support for dynamic workflows.
- Caching strategies may face memory pressure in high-concurrency scenarios.
Future Work
Future work could extend support to dynamic workflows and multi-model collaboration while improving cache management for complex scenarios.
AI Executive Summary
Helium is a workflow-aware LLM serving framework that significantly improves efficiency through proactive caching and optimized scheduling. Existing systems like vLLM focus on single-call optimization, neglecting cross-call redundancies. Helium models workflows as query plans, identifies shared structures using templated radix trees, and optimizes scheduling via cost models. Experiments show Helium achieves a 1.34x speedup in complex financial tasks and up to 1.56x in simpler workflows. This research enhances scalability for agentic workflows and provides new directions for LLM serving systems. While currently limited to single-model workflows, future extensions could address multi-model scenarios.
Deep Analysis
Background
The rise of LLMs in agentic workflows has exposed inefficiencies in existing serving systems like vLLM, which fail to optimize cross-call redundancies. These workflows involve complex dependencies and repeated computations.
Core Problem
Existing systems optimize single LLM calls but overlook workflow-level redundancies, leading to resource waste and performance bottlenecks.
Innovation
Helium addresses cross-call redundancies via proactive caching and cache-aware scheduling. Key innovations include: 1) proactive KV caching; 2) templated radix trees for shared structure identification; 3) cost-based scheduling optimization.
Methodology
- �� Model workflows as query plans to identify operator dependencies.
- �� Use templated radix trees to analyze shared structures and optimize KV cache reuse.
- �� Apply cost-based scheduling to maximize GPU utilization and cache efficiency.
Experiments
Experiments include financial analysis and multi-agent collaboration scenarios, comparing Helium against vLLM. Metrics include speedup ratios, cache hit rates, and GPU utilization.
Results
Helium achieves a 1.34x speedup in complex financial workflows and up to 1.56x in primitive workflows. Proactive caching significantly reduces redundant computations and optimizes resource utilization.
Applications
Applicable to scenarios requiring efficient processing of complex workflows, such as financial analysis, multi-agent collaboration, and real-time data processing.
Limitations & Outlook
Currently supports single LLM workflows, with limited dynamic workflow handling. Caching strategies may face challenges in high-concurrency environments.
Plain Language Accessible to non-experts
Think of Helium as a smart kitchen assistant. It preps commonly used ingredients (proactive caching) and optimizes the cooking order (scheduling optimization), avoiding repeated chopping or wasted time. This makes the entire kitchen run efficiently.
ELI14 Explained like you're 14
Imagine playing a multiplayer game where each character has tasks to complete. Helium is like a super team leader who preps all the tools each player needs and arranges the best action order to finish faster! Cool, right?
Glossary
KV Cache
Stores contextual states from LLM calls to avoid redundant computations.
Used to optimize cross-call shared structures.
Proactive Cache
Pre-stores predicted shared data to reduce redundancy.
Helium implements proactive caching by analyzing workflow structures.
Query Plan
Models workflows as graphs of operations to optimize execution order.
Helium treats agentic workflows as query plans.
Templated Radix Tree
A data structure for identifying shared prefixes in workflows.
Used for cache-aware scheduling optimization.
Cache-Aware Scheduling
Optimizes operation allocation and execution order based on cache states.
Helium uses cost models for this scheduling.
Open Questions Unanswered questions from this research
- 1 How to support dynamically generated workflow structures?
- 2 How to optimize cache management in multi-model collaboration scenarios?
Applications
Immediate Applications
Financial Analysis
Optimizes complex data workflows for improved analysis efficiency.
Multi-Agent Collaboration
Supports efficient task allocation and execution across multiple roles.
Long-term Vision
Dynamic Workflow Optimization
Extends support to dynamically generated complex scenarios for broader applications.
Abstract
Agentic workflows are composed of sequences of interdependent Large Language Model (LLM) calls, and they have become a dominant workload in modern AI systems. These workflows exhibit extensive redundancy from overlapping prompts and intermediate results due to speculative and parallel exploration. Existing LLM serving systems, such as vLLM, focus on optimizing individual inference calls and overlook cross-call dependencies, leading to significant inefficiencies. This paper rethinks LLM and agent serving from a data systems perspective and introduces Helium, a workflow-aware serving framework that models agentic workloads as query plans and treats LLM invocations as first-class operators. Helium integrates proactive caching and cache-aware scheduling to maximize reuse across prompts, KV states, and workflows. Through these techniques, Helium bridges classic query optimization principles with LLM serving, achieving up to 1.56x speedup over state-of-the-art agent serving systems on various workloads. Our results demonstrate that end-to-end optimization across workflows is essential for scalable and efficient LLM-based agents.