GPU cluster performance isn’t just about the GPUs. In order to gain an advantage in speed of innovation, each layer of the stack must be considered as a variable that will either improve or degrade your overall performance. Given the massive data sets required for training and inference workloads, storage is a critical piece of the puzzle. There has to be enough capacity and the data needs to move as quickly as your GPUs can process it. (*Clearly network throughput plays a role here but we’ll come back to that later.)
Selecting the appropriate storage solution directly influences performance, efficiency, and overall project scalability. This blog discusses three industry-leading storage solutions - Ceph, VAST, and WEKA - and provides an overview of their strengths, capabilities, and ideal applications for AI environments.
CEPH: FLEXIBLE, OPEN-SOURCE STORAGE
Ceph stands out as an open-source, software-defined storage solution renowned for its scalability and flexibility. It uniquely offers unified support for block, file, and object storage, making it versatile for diverse IT environments.
Key Strengths:
- Unified and Scalable:
Ceph effectively handles multiple data types, scaling seamlessly from small deployments to multi-petabyte clusters. - Community-Driven Innovation:
Backed by a robust community, Ceph consistently evolves to meet diverse storage requirements, benefiting from wide-ranging industry expertise. - Cost Efficiency:
Ideal for organizations mindful of their infrastructure investments, Ceph operates well on commodity hardware without compromising scalability.
Ideal Use Cases:
- Organizations that require extensive, cost-effective scalability, especially for varied workload types.
- Educational institutions and research facilities leveraging open-source platforms.
- Enterprises building private cloud environments needing unified storage capabilities.
VAST: PERFORMANCE-DRIVEN UNIFIED STORAGE
VAST Data combines high-performance capabilities with a simplified approach to data management. Its innovative all-flash architecture addresses the needs of modern AI-driven workloads, offering significant throughput and data efficiency.
Key Strengths:
- Outstanding Throughput:
VAST Data delivers impressive throughput metrics, with benchmarks demonstrating capabilities beyond 140 GB/s, making it suitable for demanding AI and analytics workloads. - Efficient Unified Architecture:
Supporting NFS, SMB, and S3 protocols, VAST enables simplified storage management across diverse data types. - Data Efficiency:
Advanced data reduction and optimization capabilities enhance overall storage utilization, providing economic value even in expansive storage environments.
Ideal Use Cases:
- Enterprises running large-scale AI training environments and data analytics platforms.
- Industries handling extensive datasets, such as media production and financial analytics, benefiting from rapid data accessibility.
- Organizations aiming for future-ready, performance-oriented infrastructure.
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WEKA: OPTIMIZED STORAGE HIGH-PERFORMANCE COMPUTING
WEKA is specifically designed for high-performance computing and intensive AI workloads. Renowned for its exceptionally high IOPS and minimal latency, WEKA provides unmatched responsiveness and scalability for complex computational tasks.
Key Strengths:
- Superior Performance:
Benchmarked with sustained throughputs exceeding 600 GB/s and 5 million IOPS at sub-millisecond latency, WEKA meets the most rigorous AI processing demands. - Hybrid and Cloud Flexibility:
WEKA’s platform integrates seamlessly into cloud and hybrid setups, supporting versatile deployment strategies. - Simplified Management:
Despite its robust architecture, WEKA maintains user-friendly management interfaces, making sophisticated storage technology approachable for diverse IT teams.
Ideal Use Cases:
- AI research and development teams requiring ultra-high-performance storage solutions.
- Life sciences, genomics, and autonomous vehicle companies where real-time data processing and analytics are mission-critical.
- Organizations adopting hybrid cloud strategies to maintain flexibility and performance.
SIDE BY SIDE
| Feature |  |
 |
 |
|---|---|---|---|
| Architecture | Open-source, distributed | All-flash unified | HPC-optimized file system |
| Performance | Scalable, flexible performance | High throughput, efficient | Highest throughput, ultra-low latency |
| Scalability | Highly scalable | Scalable from TB to EB | Effectively scalable across environments |
| Ease of Management | Requires specialized expertise | Simplified user experience | User-friendly and accessible |
| Ideal Environments | Varied workloads, budget-conscious | Enterprise AI and analytics | Intensive AI and HPC workloads |
ALIGN STORAGE CHOICES WITH AI GOALS
Selecting the right storage solution depends on the specific AI workload demands, performance goals, and organizational infrastructure strategies. Ceph offers versatile scalability and cost efficiency ideal for diverse workload environments. VAST Data stands out with its unified, high-performance approach, especially suited to demanding enterprise applications. WEKA excels in environments where ultra-high performance, minimal latency, and versatile cloud integration are essential.
By carefully matching storage solutions to precise requirements, enterprises can ensure optimal performance, future scalability, and effective resource allocation, empowering AI initiatives to succeed at scale.
WHITEFIBER: AI INFRASTRUCTURE WITH FLEXIBLE STORAGE OPTIONS
WhiteFiber offers a variety of AI storage options, including WEKA, Vast, Ceph in order to provide petabytes of custom high-performance storage without ingress or egress costs–accessible from every machine via GPUDirect RDMA.
- High-Performance for deep learning workloads:
Achieve up to 40 GBps single-node and 500 GBps multi-node read performance—ideal for massive datasets like 4K images or trillion-parameter NLP models. - Accelerated I/O with GPUDirect storage:
NVIDIA GPUDirect Storage® enables 40+ GBps direct data transfer to GPU memory, reducing latency and boosting training speed for datasets beyond cache. - Fast, fault-tolerant checkpointing:
Write speeds up to 20 GBps per node enable quick checkpointing of terabyte-scale files, minimizing training interruptions for deep learning workflows. - Optimized caching and staging:
RAM and NVMe caching delivers up to 10X faster reads, efficiently supporting diverse deep learning workloads and dataset sizes.
Learn more at https://www.whitefiber.com/cloud/storage or set up time with one of our technical experts.
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