The Storage that feeds AI training and modeling for High-Impact AI

In the second blog of this three-part series, we explore how the performance storage tier accelerates AI model training by eliminating I/O bottlenecks and maximizing GPU utilization to secure your AI investment.

If you missed the first blog " Why Data Ingest Storage is an Unsung Hero in AI and LLM Pipelines", click here to read it.

Stage 2: The Performance Storage Tier

A high-performance storage solution is essential for processing large AI models because it directly impacts the efficiency and speed of the entire workflow. It ensures that data is delivered fast enough to keep expensive GPUs fully utilized, preventing them from sitting idle and wasting resources. This extreme throughput is crucial for model training, which often involves large sequential I/O operations. By minimizing model turnaround time, high-performance storage allows for more rapid iterations and faster development cycles.

The Three-Stage Storage Model Behind AI Workflows

As part of a three-stage storage model, Stage 2 is dedicated to high-speed data staging that enables rapid access and throughput for AI workloads - particularly during data preprocessing and training phase involving large language models (LLMs).

• Stage 1 – Ingest and Curation: A capacity storage tier optimized for cost, designed to absorb and store vast volumes of raw, unstructured data from diverse sources.

• Stage 2 – Data Staging and Modeling: A performance storage tier optimized for high throughput and low latency, feeding curated datasets into models during model development and training.

• Stage 3 - Deployment: A go-live or production storage tier, where the final AI model gains enterprise-level resiliency and reliability, and is deployed to solve real business problems.

Why a Performance Tier Is Non-Negotiable for AI Workloads

The performance tier is essential to AI infrastructure because it enables the seamless flow of data through the most compute-intensive stages of the pipeline - normalization, tokenization, and training. Large language models (LLMs) require access to petabytes of high-quality, preprocessed data, and without fast, reliable storage, even the most powerful GPUs can sit idle waiting for input. The performance-tier storage ensures that data moves smoothly into memory, models checkpoint reliably, and logs and metrics are captured in real time. By eliminating I/O bottlenecks and supporting scalable throughput, it keeps GPUs fully utilized and accelerates the entire training cycle - turning infrastructure into a competitive advantage.

Common Storage Types for the AI Performance Tier:

• Parallel File Systems (e.g., Lustre, GPFS): These systems are designed for extreme performance with massive concurrency, making them ideal for synchronous I/O-intensive AI workloads.

• Distributed File Systems (e.g., JuiceFS, 3FS, Ceph): Offering cloud-native flexibility, these systems can provide high performance while also offering features like object storage back-ends and hybrid access modes.

• NVMe Block Storage (Local and networked SSDs): Offering straightforward deployment and low latency when directly attached to training nodes, but coordinating replication and orchestration across nodes can become complex.

The Business and Team Benefits of a Strong AI Performance Tier

Investing in a high-performance storage tier for training models translates into tangible advantages for both the business and the AI development team. It accelerates the final stage of data preparation by transforming and reshaping curated inputs into training-ready datasets, enabling rapid training submission with minimal delay. Training cycles become faster and more efficient, with high throughput eliminating GPU starvation and reducing checkpointing overhead - leading to quicker insights and more frequent model iterations. The performance storage tier also ensures that costly GPU infrastructure is fully utilized, maximizing the return on investment. Beyond performance, it establishes a reliable and scalable foundation that effortlessly adapts to increasing data volumes and model complexity, supporting long-term growth and innovation.Shape

Feeding the GPUs: From data preprocessing to training

A walkthrough of an AI/LLM data pipeline leading to deployment:

Collection → Cleaning → Enrichment → Normalization → Tokenization → Training

Performance storage powers critical AI training stages - normalization, tokenization, and model training - by delivering the high throughput and concurrent access needed to keep data flowing and GPUs fully engaged.

High-Performance Training Storage is an AI must-have

Without a robust performance storage layer, slow centralized storage causes crippling I/O bottlenecks, expensive GPUs sit idle, and debugging and iteration cycles become slow. In contrast, with the right performance storage, data is staged on high-speed SSDs or NVMe-based parallel file systems, and high IOPS and low latency continuously feed data to GPUs, leading to faster model iteration and a reduced time to deployment.

Without a robust high-performance storage layer, the storage architecture quickly becomes a bottleneck - crippling I/O throughput, expensive GPUs sit idle, and dragging down debugging and iteration cycles. In contrast, with the right storage architecture - leveraging high-speed NVMe-based SSDs and parallel file systems - data is efficiently staged and delivered with high IOPS and low latency. This ensures GPUs are continuously fed, enabling faster model iteration and significantly reducing time to deployment.

Without a robust performance storage layer:

• Slow centralized storage causes crippling I/O bottlenecks.

• Expensive GPUs sit idle, wasting valuable compute resources.

• Debugging and iteration cycles become slow and inefficient.

With the right performance storage in place:

• Data is staged on high-speed SSDs or NVMe-based parallel file systems.

• High IOPS and low latency continuously feed data to GPUs.

• Model iteration is significantly faster, leading to a reduced time to deployment.

Accelerate AI Training with the Right Storage Infrastructure

The ideal performance storage architecture often depends on the specific characteristics of the model training workload, but some general storage performance tier requirements apply. For a performance storage solution to be effective in an AI pipeline, it must meet several technical requirements:

• Massively Parallel I/O Supports thousands of concurrent data requests from GPU cores without bottlenecks, enabling high-throughput data streaming under extreme concurrency.

• High Throughput & Low Latency Delivers sustained bandwidth and minimal latency for loading large datasets, writing periodic checkpoints, and logging metrics and model parameters.

• Coordinated multi-initiator parallel reads Allows distributed training by multiple GPUs or TPUs across nodes to access training data simultaneously without bottlenecks.

• AI-Native Storage Access Integrates with POSIX-compliant parallel file systems (e.g., Lustre, Spectrum Scale (GPFS)) for HPC-style training, and supports GPUDirect Storage for direct GPU access to data without CPU intervention.

• Scalable Architecture Enables flexible scaling both up (adding drives for capacity/performance) and out (adding nodes for distributed parallelism), accommodating multi-petabyte datasets and growing compute clusters.

• Resilience & Fault Tolerance Designed to eliminate single points of failure, allowing training jobs to resume or restart without data loss in the event of hardware or software interruptions.

Matching Storage Solutions to Model Training Workloads

AI training scenarios - whether large-scale, small/medium-scale, or cloud-based - vary widely depending on workload and environment. A range of high-performance file systems exists, each with distinct characteristics architected for specific use cases.

• Developers training large-scale language models across hundreds or thousands of GPUs often rely on high-performance parallel file systems like Lustre or GPFS. These systems deliver ultra-fast data access, ensuring that expensive compute resources remain fully utilized during active AI workloads - particularly in deep learning and I/O-intensive tasks. They also enable rapid, scalable checkpointing to maintain training continuity and resilience.

• For mid-sized or iterative models, developers often prefer distributed file systems like JuiceFS, 3FS, or Ceph, which offer hybrid access modes and enable a smooth transition from training to inference, especially in containerized environments.

• Public cloud environments, such as Azure, AWS, and GCP, frequently use block storage as high-performance scratch space for staging data, checkpoints, and model outputs, which is ideal for tightly scoped training jobs. However, these providers are rapidly integrating parallel file system services into their environments, recognizing the advantages of scalable throughput and efficiency that parallel file systems offer over traditional block storage for large-scale AI workloads.

Conclusion – Performance Storage: The Bedrock of AI Innovation

In the quest for AI breakthroughs, the spotlight often shines on algorithms and compute power. However, high-performance storage is the unsung hero, the hidden driver of speed, efficiency, and scalability in modern AI pipelines. It's the core that enables rapid preprocessing, nonstop GPU utilization, and reliable checkpointing, making it foundational to AI success. By combining ultra-fast NVMe SSDs, parallel and distributed file systems, and AI-native integration, performance storage transforms infrastructure from a potential bottleneck into a significant strategic advantage in the race to build the next generation of intelligent applications.

Don’t miss the third and final blog in this series: Delivering Production AI at Scale with the Right Storage.