Beyond Peak Performance: What Storage Systems Do We Need in the Era of Large Models?
Therise of Large Language Models (LLMs) and the impending era of intelligent agentsare ushering in a new era of data-driven innovation. Storage systems, the critical infrastructure underpinning artificial intelligence, face a dual challenge: handling massive data storage needswhile ensuring high-speed access and processing capabilities to support complex machine learning model training and inference tasks.
What kind of storage systems do we need in thisage of large models? To answer this question, InfoQ’s Geek Appointment X QCon livestream recently brought together a panel of experts, including Datastrato Founder & CEO Du Junping as moderator, ByteDance technical expert Li Jinglun, JuiceFS partner Su Rui, and OPPO distributed storage expert Chang Liang, to delve into the design and challenges of next-generation storage systems.
Here are some key insights from their discussion:
The Bottleneck Lies inCommunication, Not Storage
The panel agreed that the primary bottleneck in AI workloads isn’t storage itself, but rather the communication between computing nodes. This highlights the need for storage systems that can efficiently handle data transfer and access requests.
File Storage: The Unsung Hero
While block and object storage oftentake center stage, file storage, often overlooked, plays a crucial role in AI development. Its history dates back to the 1980s, but the lack of dedicated file storage solutions tailored for AI presents a significant challenge.
Leveraging Object Storage for Data Persistence
The panel emphasized the importance ofoffloading data persistence tasks to object storage services. This allows file storage systems to focus on providing efficient data access and management.
Data Lifecycle Management: A Key to Efficiency
Implementing data lifecycle management strategies, including data migration based on usage patterns, is crucial for optimizing storage costs and performance. This allows for theseamless movement of data between different storage tiers, from hot to cold, based on access frequency.
Performance vs. Efficiency: Finding the Right Balance
The panel stressed that users should not prioritize peak performance at the expense of overall efficiency. Instead, they should focus on selecting storage solutions that offer a balance between performance, cost, and scalability.
Looking Ahead: The Future of Storage for AI
The QCon Global Software Development Conference 2024 in Shanghai will feature a dedicated track on Next-Generation DataforAI Technology Architecture. This track will delve into topics such as data asset identification and management, data governancebest practices, and real-world case studies showcasing how different industries are leveraging data and AI for innovation and transformation.
The panel’s discussion underscores the critical role of storage systems in the evolving landscape of AI. By understanding the unique challenges and opportunities presented by large models, we can design and implement storage solutions that empowerthe next generation of AI applications.
References:
- QCon Global Software Development Conference 2024 Shanghai: https://qcon.infoq.cn/2024/shanghai/schedule
- InfoQ Geek Appointment X QCon livestream: [Link to livestream recording] (Please provide the link if available)
Views: 0