Efficient Hyperscale LLM Inference System based on Scale-out Context Memory

Overview

Large Language Models (LLMs) are rapidly evolving toward longer context windows, advanced reasoning capabilities, personalization, and long-term memory support. However, the explosive growth of context length has made KV cache management a critical bottleneck, consuming a substantial portion of GPU memory and limiting the scalability and cost efficiency of modern AI infrastructure.

This project aims to develop a Scale-out Context Memory System that extends LLM context storage beyond GPU memory by leveraging heterogeneous memory resources, including DRAM, CXL-attached memory, non-volatile memory (NVM), SSDs, and remote memory. By treating context as a first-class system resource rather than a passive data object, we seek to overcome fundamental memory limitations in large-scale LLM serving environments.

Research Goals

The project investigates system-wide optimization techniques for managing large-scale context memory across heterogeneous hardware resources. Our primary objectives include:

• Designing scalable context memory architectures that extend beyond GPU VRAM.
• Developing runtime systems for context placement, migration, and scheduling.
• Enabling efficient context tiering across memory and storage hierarchies.
• Optimizing performance, energy efficiency, and service-level objectives (SLOs) simultaneously.
• Building an integrated orchestration framework for large-scale AI inference infrastructure.

Research Plan

Year 1: Foundation of Context Memory Systems

• Analyze context access patterns across prefill and decode phases.
• Develop lightweight monitoring and profiling mechanisms.
• Build a prototype disaggregated context memory system.
• Investigate GPU access to remote memory using RDMA-based communication.
• Establish power and resource utilization models for LLM inference.

Year 2: Scale-out Memory Expansion and Runtime Control

• Expand context memory across multiple nodes using VRAM and DRAM.
• Develop hotness-aware context placement and migration mechanisms.
• Design context-aware scheduling policies for heterogeneous memory environments.
• Introduce runtime control techniques for SLO stabilization and peak power reduction.

Year 3: Heterogeneous Memory-Storage Integration

• Extend context memory to CXL memory, NVM, and SSD storage tiers.
• Develop context tiering and intelligent data movement mechanisms.
• Design power-aware scheduling and resource orchestration frameworks.
• Build a complete scale-out inference platform supporting large-scale context management.

Expected Impact

This project establishes a new research direction at the intersection of AI systems, operating systems, distributed systems, memory architecture, storage systems, and networking.

The resulting technologies will enable:

• Cost-efficient deployment of next-generation LLM services.
• Support for extremely large context windows and long-term memory applications.
• Improved GPU utilization and reduced infrastructure costs.
• Energy-efficient operation of AI datacenters.
• Scalable infrastructure for future AI agents, RAG systems, and multimodal AI services.

Through this research, we aim to advance the state of the art in AI infrastructure and establish foundational technologies for future hyperscale LLM serving platforms.