What storage options are best for GPU as a Service workloads?

GPU as a Service (GPUaaS) workloads demand high-throughput, low-latency storage to feed data directly to GPUs without bottlenecks, enabling efficient AI training, inference, and HPC tasks. Optimal options prioritize NVMe-based SSDs, GPUDirect Storage, and scalable object storage integrated with parallel file systems.

Top Storage Options for GPUaaS Workloads:​

High-Performance Block Storage (NVMe/NVMe-oF): Local NVMe SSDs or NVMe over Fabrics for ultra-low latency (<10μs) and throughput >100GB/s, ideal for active datasets in training.​

GPUDirect Storage (GDS): Bypasses CPU for direct GPU memory access, using RDMA protocols; essential for NVIDIA H100/L40S GPUs in Cyfuture Cloud.​

Parallel/Distributed File Systems (e.g., Lustre, GPFS): For massive-scale datasets, offering petabyte scalability and parallel I/O up to TB/s.​

Object Storage with Tiering (e.g., S3-compatible): Cost-effective for archival/cold data, with caching to hot NVMe tiers; Cyfuture Cloud integrates seamlessly.​

All-Flash Arrays with DPU Acceleration: BlueField DPUs for encryption, deduplication, and NVMe-oF, maximizing GPU utilization in shared environments.​

Why Storage Matters for GPUaaS

GPUaaS on platforms like Cyfuture Cloud processes terabytes of data per hour for ML models, where storage I/O often limits performance more than GPU compute. High-bandwidth needs (e.g., 1TB/s aggregate for multi-GPU clusters) require defining exact throughput, latency tolerances, and data access patterns upfront. Cyfuture Cloud's GPU instances pair with NVMe-attached volumes and GDS-enabled storage to minimize data movement overhead, supporting workloads like NVIDIA DGX/HGX servers.​

Existing systems must be assessed for PCIe settings, network fabrics (e.g., InfiniBand/RoCE), and scalability before deployment. Optimization techniques include tiering (hot data on NVMe storage, cold on object), caching, and software like Peak:AIO for GPU-optimized distributed storage.​

Recommended Storage Architectures

For Cyfuture Cloud GPUaaS, hybrid architectures excel:

These leverage Cyfuture's secure data centers with NVIDIA-certified setups, ensuring 99.99% uptime.​

Cyfuture Cloud-Specific Advantages

Cyfuture Cloud optimizes GPUaaS with flexible storage: instance snapshots for GPU-generated data (checkpoints, logs), NVMe SSDs for EBS-like volumes, and object storage for backups. Robust APIs integrate GDS for direct H100 data paths, while tools like NVIDIA DCGM monitor I/O. Compared to on-prem, GPUaaS reduces TCO by 40-60% via pay-per-use, avoiding upfront NVMe array costs.​

Implementation Best Practices

Define requirements: throughput (e.g., 100GB/s+ per GPU), latency (<50μs), capacity (short/long-term). Mount storage via Linux/Windows protocols, optimizing with memory-mapped files and parallel loaders for ML datasets. Roll out with TCO analysis, starting small then scaling via orchestration.​

Conclusion

For GPUaaS workloads on Cyfuture Cloud, NVMe/GDS block storage combined with parallel file systems and tiered object storage delivers the best balance of performance, scalability, and cost. This setup unleashes full GPU potential for AI/HPC, with Cyfuture's integrations minimizing latency and maximizing ROI. Prioritize GDS-enabled NVMe for 2-5x faster data transfer over traditional paths.​

Follow-Up Questions with Answers

Q1: How does GPUDirect Storage work with Cyfuture Cloud?
A: GDS enables direct storage-to-GPU transfers via NVMe/NVMe-oF, bypassing CPU; Cyfuture supports it on H100 clusters for 90%+ bandwidth efficiency.​

Q2: What are backup strategies for GPUaaS data?
A: Use instance snapshots of NVMe volumes and object storage for checkpoints/models; Cyfuture automates point-in-time copies with minimal downtime.​

Q3: Can object storage handle real-time GPU inference?
A: Yes, with caching and prefetching; tier to NVMe for hot data, keeping costs low for cold archives in Cyfuture's S3-compatible service.​

Q4: How scalable is NVMe-oF for multi-GPU setups?
A: Highly, supporting TB/s over RoCE/InfiniBand; ideal for Cyfuture's DGX-like scaling with DPU offload for dedup/encryption.​