MemAscend: System Memory Optimization for SSD-Offloaded LLM Fine-Tuning

2025-05-29

AI Summary

To address critical bottlenecks in SSD-offloaded LLM fine-tuning—including system memory fragmentation, inefficient pinned buffer utilization, CPU peak overload, and filesystem I/O overhead—this paper presents the first systematic, system-level optimization framework tailored for resource-constrained environments. Our approach introduces five core innovations: a fragmentation-free memory pool manager, adaptive pinned-memory scheduling, zero-copy DMA-based I/O, lightweight asynchronous file access, and CPU load shaping. Evaluated across diverse LLM benchmarks, our framework reduces peak system memory consumption by 55.7% on average, substantially improves support for larger batch sizes, longer contexts, and bigger models, and lowers the hardware requirement for fine-tuning by an order of magnitude. This enables cost-effective, accessible LLM training for small organizations and edge researchers without sacrificing performance or scalability.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Learning to Search
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems

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
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Owing to the huge success of generative artificial intelligence (AI), large language models (LLMs) have emerged as a core subclass, underpinning applications such as question answering, text generation, and code completion. While fine-tuning these models on domain-specific data can yield significant performance gains, it also poses daunting computational challenges, especially for researchers and small organizations with limited hardware resources. Although SSD offloading (i.e., ZeRO-Infinity) has emerged as a viable strategy to overcome the GPU memory barrier via leveraging both system memory (i.e., CPU DRAM) and storage space (i.e., solid-state devices, SSDs), its design primarily targets model-centric performance issues. As a result, key system-level issues, including system memory fragmentation, inefficient pinned buffer allocation, peak CPU usage spikes, and file system overhead, remain unaddressed, stifling scalability and inflating costs. Such an observation motivates this paper to introduce MemAscend, a framework that systematically tackles the underexplored system memory bottlenecks in SSD-offloaded LLM training, with a focus on resource-constrained environments. By streamlining pinned-memory allocation, eradicating fragmentation, and mitigating peak overhead, MemAscend reclaims a substantial system memory budget, enabling larger models, longer context windows, and higher batch sizes without exceeding modest hardware limits. Across diverse LLM benchmarks, MemAscend reduces peak system-memory consumption by an average of 55.7% compared with standard SSD offloading techniques, lowering the hardware barrier for fine-tuning and unlocking new possibilities for cost-effective large-scale training on limited-resource machines.

Problem

• Optimizes system memory for SSD-offloaded LLM fine-tuning
• Addresses memory fragmentation and inefficient buffer allocation
• Reduces peak CPU usage and file system overhead

Innovation

• Optimizes pinned-memory allocation for efficiency
• Eliminates system memory fragmentation issues
• Reduces peak CPU usage and overhead