Deploying Foundation Model Powered Agent Services: A Survey

Wenchao Xu, Jinyu Chen, Peirong Zheng, Xiaoquan Yi, Tianyi Tian, Wenhui Zhu, Quan Wan, Haozhao Wang, Yunfeng Fan, Qinliang Su, Xuemin Shen·December 18, 2024

Summary

The text discusses advancements in large language models (LLMs), focusing on their deployment, optimization, and adaptation across various computing environments. Key points include: - Deploying LLM-based agent services across heterogeneous devices, optimizing resources for reliability and scalability. - Strategies for inference acceleration, model and resource optimization, and prominent LLMs. - A framework for understanding LLM-powered agent services in edge-cloud environments, addressing challenges like varying query loads and diverse service requirements. - Techniques for model deployment, agent components, and system optimizations, aiming for real-time services with high Quality of Service. - Various computing resources and their features for edge computing, including FPGAs, ASICs, and in-memory computing, with approaches to optimize computational efficiency. - Advancements in managing large language models, focusing on memory bandwidth optimization, efficient serving systems, mixture-of-experts models, and communication optimization. - Frameworks and tools for optimizing LLMs and resource allocation in edge-cloud environments, addressing challenges in training, deployment, and scaling. - Parallel methods for optimizing the performance of large language models and deep neural networks across different computing environments, focusing on strategies like data, model, pipeline, and tensor parallelism. - Quantization methods for reducing computational costs and memory requirements in LLMs, balancing accuracy and hardware efficiency. - Model adaptation and distillation techniques for improving model performance, efficiency, and robustness across diverse scenarios. - Token reduction methods for optimizing transformer models by reducing sequence length and computational load, addressing challenges like information loss and performance-efficiency trade-offs. - External API and tool integration for enhancing LLMs' decision-making, adaptability, and safety, enabling access to current information and interaction with proprietary systems. - Batching methods for optimizing serving systems by grouping similar requests, improving throughput and reducing resource consumption. - Innovations in large language models, including advancements in scaling, optimization, and applications across various domains, such as education, reasoning, multimodal understanding, and long-term memory augmentation.

Key findings

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Introduction
Background
Overview of large language models (LLMs)
Importance of LLMs in modern computing environments
Objective
Focus on deploying, optimizing, and adapting LLMs across various computing environments
Deployment Strategies
Heterogeneous Device Deployment
Challenges and considerations for deploying LLMs on diverse devices
Optimization techniques for reliability and scalability
Inference Acceleration
Model and resource optimization methods
Prominent LLMs and their deployment scenarios
Edge-Cloud Frameworks
Edge-Cloud Environment Understanding
Framework for LLM-powered agent services
Addressing challenges like varying query loads and diverse service requirements
Model Deployment and Optimization
Computing Resources for Edge Computing
Features and optimization approaches for FPGAs, ASICs, and in-memory computing
Real-time services with high Quality of Service
Large Language Model Management
Memory Bandwidth Optimization
Efficient serving systems for LLMs
Mixture-of-experts models and communication optimization
Frameworks and Tools for Optimization
Edge-Cloud Resource Allocation
Tools and frameworks for optimizing LLMs and resource allocation
Addressing challenges in training, deployment, and scaling
Parallel Methods for Optimization
Performance Optimization Across Environments
Strategies for data, model, pipeline, and tensor parallelism
Enhancing LLM and deep neural network performance
Quantization Techniques
Reducing Computational Costs
Methods for reducing computational requirements in LLMs
Balancing accuracy and hardware efficiency
Model Adaptation and Distillation
Improving Model Performance
Techniques for enhancing model efficiency and robustness
Strategies for adapting models across diverse scenarios
Token Reduction Methods
Optimizing Transformer Models
Approaches for reducing sequence length and computational load
Addressing challenges like information loss and performance-efficiency trade-offs
External API and Tool Integration
Enhancing Decision-Making and Safety
Integration of external APIs and tools for LLMs
Enabling access to current information and interaction with proprietary systems
Batching Methods
Optimizing Serving Systems
Grouping similar requests for improved throughput and resource consumption
Innovations in Large Language Models
Advancements in Scaling, Optimization, and Applications
Innovations across domains like education, reasoning, multimodal understanding, and long-term memory augmentation
Basic info
papers
distributed, parallel, and cluster computing
artificial intelligence
Advanced features
Insights
What are some of the key techniques and frameworks mentioned for optimizing large language models and resource allocation in edge-cloud environments?
Which computing resources and features are highlighted for edge computing in the context of large language models?
What are the main strategies for inference acceleration and model optimization discussed in the text?