What is AI Storage
AI storage refers to storage solutions that are capable of handling artificial intelligence (AI) workloads, including machine learning (ML), deep learning, large language models (LLMs), and generative AI (GenAI). These storage solutions are engineered to handle the large volumes of data generated and consumed by AI applications, providing the necessary high performance, scalability, and data management capabilities to facilitate efficient model training and inference.
AI Storage Challenges
Scalability
AI and machine learning projects often start small and expand as they prove successful. As these projects scale, the data needed to train these models grows at unprecedented rates. As the data in AI projects grows, both the performance and capacity requirements for the storage system increase. More performance is needed because additional GPUs must be used concurrently for training. Also, more capacity is required because all the data needs to be stored in one system as models need to be trained on these large datasets.
However, scalable performance and capacity are not enough. When AI projects expand, the storage system’s manageability and operations also need to scale. AI projects are complex, and managing and operating an ML storage system that fails to scale efficiently can become a bottleneck, limiting the growth of AI initiatives.
Performance
From model training to tuning and testing, the storage system’s performance can significantly impact every step of the AI training. For example, training AI models, specifically in deep learning and GenAI, requires processing large amounts of data. Slow deep learning storage or GenAI storage can become a bottleneck, causing GPUs and other processing units to remain idle, extending training times and increasing costs.
Deep neural networks and LLMs require high-performance storage for yet another reason: Checkpointing. These models are very large and complex, and it may take them several weeks to complete a training job; for that reason, checkpointing is required to save the current state of the models after some training is done. Since model training is parallelized, when checkpointing is done, the model’s current state is saved to the storage from all nodes simultaneously, requiring very high peak throughput to minimize the checkpointing time.
Unavailability
Unavailability and downtime during training, tuning, and testing can frustrate data scientists by interrupting their workflows and stopping them from making progress. Unplanned downtime can also yield data loss or corruption and lost progress in a training job, negatively impacting the accuracy of models.
Downtime and maintenance windows can cause a significant waste of resources and money, i.e., GPU time. When data can’t be accessed, expensive resources like GPUs, CPUs, and personnel become idle and still incur costs. In addition, downtime during inference can cause revenue loss, among other issues, whenever services are down.
Complexity
Many AI storage solutions are appliance-based, adding complexity to the infrastructure’s operations. Appliances involve custom hardware, forcing admins to learn to manage something new. Having to work with multiple appliances makes management more challenging because each has a specific configuration and other management methods. In addition, each appliance needs to be properly maintained and upgraded to work as expected.
Complex storage systems are challenging to operate at scale and cost more. Scaling complex AI storage systems will always require more manpower as configurations and maintenance become more difficult with more resources. In the end, complexity forces organizations to spend more money on hardware management and more staff.
Security
Security in storage systems is a critical challenge for AI workloads due to the sensitive and valuable nature of the data involved. AI storage systems often process and store large volumes of sensitive data, including personal information, proprietary business data, or intellectual property. Insecure storage systems can be vulnerable to data breaches, leading to the exposure of confidential information.
Moreover, many AI applications are subject to regulatory requirements that dictate how data must be handled and protected. Inadequate security in storage systems can lead to compliance violations, exposing companies to potential fines and legal actions.
Storage Requirements for AI Projects
To address the challenges mentioned above, organizations must implement storage systems that offer the following:
#1 Linear Scale-out for Capacity and Performance
Scale-out is crucial in machine learning because training needs to be done in a distributed manner. A single GPU or even a box of interconnected GPUs is too slow for big problems; therefore, distributed training is required to enhance performance. A distributed scale-out parallel file system can reduce the time it takes to access and process data, directly speeding up the training phase.
In the scale-out world, performance is relative; organizations should be able to grow their storage system to deliver more performance simply by adding more hardware. Scale-out storage allows them to use multiple servers and aggregate their performance rather than a single bigger server. For that reason, with a scale-out system, they don’t need to worry about having the fastest and latest servers because they can just add standard servers whenever their applications require more performance.
Similarly, organizations can easily grow their capacity with a scale-out file system. AI workloads require very large datasets, so the storage system needs to enable organizations to expand the capacity as soon as it is required or on short notice.
#2 Focus on High Bandwidth, Not Latency
For AI workloads, it’s a throughput game; there is no “low latency” for these applications. GPUs are too fast for everything in a modern data center, such as local storage and especially anything over the network. It doesn’t matter how fast the network file system is; it will always respond too slowly for GPUs, so prefetching must be done. Prefetching masks latency by anticipating data needs, so only bandwidth matters in AI applications. High bandwidth helps ensure data is always coming in, so the GPUs are never idle.
#3 No Downtime Ever
Eliminating downtime completely is crucial for AI workloads because interruptions are costly. Settling for high-speed storage solutions that lack reliability is the road to disaster. Reliability and a system that’s forgiving to operator errors are crucial. Ensuring the storage system is fast and reliable is key to maintaining continuous operation and efficiency in AI tasks.
#4 More Storage With Smaller Teams
AI projects require storage solutions that are easy to run at the scale of 100s of petabytes due to the unique demands of AI workflows, which include managing large volumes of data, ensuring high performance, and maintaining flexibility as projects evolve. Data constantly grows with AI projects, so storage solutions must offer easy data management and operations so smaller teams can efficiently manage them.
#5 Data Security
Data is the most essential component in AI applications and must be protected. Storage solutions must incorporate strong security measures to prevent unauthorized access and compliance with frameworks like HIPAA. Some security measures include ACLs for fine-granular access control, end-to-end data encryption, and multi-tenancy for organizations working with multiple customers or groups. In general, storage must provide robust security features because the overall security is just as good as the weakest link in the organization.
By considering these requirements when choosing a storage system for AI, organizations can avoid typical mistakes that dramatically increase cost, cause user frustration, and threaten the success of AI projects in the future.
Why Quobyte Is the Best Solution for AI Workloads
Quobyte is uniquely positioned to provide the performance and simplicity to scale that AI projects require. As a scale-out distributed parallel file system, Quobyte enables large-scale AI projects by providing high-performance storage while helping organizations optimize costs.
Scale-Out Storage With Linear Performance Scaling
Quobyte’s unique scale-out architecture enables linear scaling of performance and capacity, and it can deliver arbitrary performance by adding hardware. Quobyte scales linearly, so organizations can add more capacity or performance whenever their applications demand it, allowing them to start small and expand their storage whenever they need to. It runs on commodity hardware and aggregates its performance, so organizations don’t need expensive appliances or custom hardware.
Blazing High Performance
Being a distributed parallel file system, Quobyte delivers the performance GPUs need by offering high throughput and low latency, enabling parallel data access, and eliminating bottlenecks. Quobyte enhances performance by allowing native clients to communicate directly with data, ensuring low latency and eliminating bottlenecks typically associated with NFS gateways, all without the pain kernel modules.
Furthermore, Quobyte supports RDMA and parallel IO over TCP to optimize throughput. It achieves up to 100 GB/s in single stream read performance using RDMA on Linux, and up to 4 GB/s on Windows systems using TCP for single stream reads.
Zero Disruptions
Quobyte’s architecture eliminates downtime, making everything non-disruptive. Companies can add hardware, perform updates, move data, and much more without downtime or maintenance windows. Quobyte enables data scientists to access and work with their data whenever they need it and GPUs to work 24/7 to maximize the return on companies’ investments. Additionally, zero disruptions make the life of admins easier because they can work in a more stable and predictable environment.
Easy to Run at Scale
Quobyte’s operational simplicity allows small teams to effortlessly manage clusters over 100 PB in size. This ease of management is due to several factors: there are no required maintenance windows, it utilizes standard hardware and Linux systems, and it operates without the need for kernel modules. Because it runs on standard servers, admins can handle the hardware using familiar techniques without the necessity to acquire new skills.
Quobyte is also highly automated and self-healing, so whenever there is a hardware failure or any issue with the cluster, Quobyte takes care of it without requiring immediate human intervention.
Efficient Data Management
Quobyte offers a powerful policy engine to manage data efficiently. Admins can create different policies to define where files should be placed, such as whether they want data to be placed on NVMe or HDD or protected with replication, erasure coding, or a combination of both. They can do that with the granularity of characteristics like name, extension, owner, size, and much more.
Quobyte also offers a Fast File Metadata Query engine that lets users quickly scan billions of files for all kinds of metadata. The file query engine allows quick finding of the correct files, including user-defined metadata (extended attributes, S3 user metadata). There is no need for extra metadata tools or databases.
In addition, Quobyte has a single namespace for all interfaces, such as Linux, Windows, MacOS, S3/Object, NFS 3/4, and much more. Users can share files seamlessly; they can modify files in one interface and see the changes in the other. Access controls are also unified; they can be set in Linux, for example, and enforced in all other interfaces.
360 Security
Whether users handle sensitive data or work with multiple customers, Quobyte keeps the data secure from unauthorized access at all times. Quobyte protects data at rest and in flight with end-to-end data encryption and offers other security features such as TLS support, X.509 certificates, and unified ACLs so users can have peace of mind that their data will always be secured.
Additionally, Quobyte provides multi-tenancy to logically isolate tenants, which can be groups, departments, projects that need to be firewalled, customers, etc. This allows tenants to feel they have their own storage system. For example, a user on tenant A would not be able to see any files or volumes on a different tenant. With Quobyte’s multi-tenancy, organizations can offer storage-as-a-service to multiple groups and customers.
Is your storage AI ready? Can your storage deliver high performance and reliability without breaking your budget? Learn more about Quobyte, the easiest parallel file system in the world, and how it can deliver the performance your AI projects and data scientists need.
