How can hybrid clouds handle complex AI workloads?
Transforming the Hybrid Cloud for Emerging AI Workloads
November 21, 2024
https://arxiv.org/pdf/2411.13239This paper envisions a transformed hybrid cloud optimized for complex AI workloads, including large language models and emerging generative AI, with an emphasis on affordability, usability, and scalability. It proposes a full-stack redesign encompassing application, middleware, platform, infrastructure, and hardware layers.
Key points relevant to LLM-based multi-agent systems include:
- THINKagents: A proposed framework for agentic AI systems, designed to improve agent collaboration, specialization, and overall intelligence through features like transactive memory.
- LLM as an Abstraction (LLMaaA): A novel paradigm using natural language as the primary interface for building, deploying, and managing complex applications. It employs a master agent (LLM) to orchestrate various other LLM and non-LLM agents for efficient task execution.
- Agentic Systems for Diverse Applications: The paper suggests using agentic systems for tasks like software development, regulatory compliance, scientific simulations (wargaming), and incident management, demonstrating their versatility. These agents would leverage the improved cloud infrastructure.
- Optimization Across the Stack: The paper highlights the need for model optimization techniques (scaling LLMs, sparse model optimization, and compiler/runtime improvements for accelerators) to improve efficiency and performance of LLM-based agents operating within this cloud environment.