don’t let storage hold you back: the AI performance boost

In the AI world, data is the new oil, and storage is the refinery. As AI models grow exponentially in size and complexity, the importance of robust, efficient storage solutions has never been more critical. The difference between a groundbreaking model and a stalled project often comes down to how quickly and efficiently you can feed data to your hungry algorithms.

Imagine trying to teach a computer to think like a human, but instead of a brain, you’re working with rows of blinking servers. This is the challenge faced by AI researchers and engineers every day. The sheer volume of data required to train modern AI models is staggering—we’re talking about libraries’ worth of information being processed at lightning speeds. Without the right storage infrastructure, even the most powerful AI systems can grind to a halt, leaving expensive GPUs twiddling their metaphorical thumbs while waiting for data.

In this guide, we’ll explore the world of AI storage, why it matters, how it works, and what you need to know to keep your AI projects running smoothly. Let’s dive in!

why does storage matter?

Imagine training a large language model like ChatGPT. It’s like teaching a student who needs to read every book in a library—repeatedly. If the storage system can’t keep up, GPUs sit idle, waiting for data, wasting expensive computing resources. For example, training a 175-billion-parameter model like Chat GPT3 required 45TB of raw text data processed over and over.

Traditional hard drives (HDDs) work for backups but struggle with AI’s random data access patterns. Solid-state drives (SSDs), especially NVMe models, are faster but costlier. Balancing speed, capacity, and cost is key.

understanding the AI data cycle

Before diving into storage solutions, it’s crucial to understand the AI Data Cycle. This continuous loop of data consumption and generation consists of six distinct stages, each with specific storage requirements:

• data collection and storage

Raw data is collected from various sources and stored securely. This stage requires high-capacity storage solutions, such as enterprise hard disk drives (eHDDs), which provide cost-effective bulk data storage.

• data processing and preparation

Data is processed, cleaned, and transformed for model training. Upgraded storage infrastructures, including fast data lakes and high-capacity enterprise solid-state drives (eSSDs), are often deployed to support this stage.

• model training

AI models are trained on high-performance supercomputers. This requires high-bandwidth flash storage near the training server, utilizing PCIe Gen 5 eSSDs for low-latency performance.

• model evaluation and validation

Trained models are tested on new data to ensure they generalize well and meet business objectives. This stage is crucial for assessing model readiness for deployment.

• AI inference engine

Deployed models operate in production environments for real-time analysis and predictions. This stage demands comprehensive data analysis and significant storage performance for timely responses.

• new content generation

AI generates new content based on insights from trained models. This generated data is stored for future use, feeding back into the cycle and driving further AI development.

Recognizing these stages will help you optimize your storage choices across your AI infrastructure.

key storage types simplified:

1. high-speed file systems (for training)

what they do:

• Handle thousands of simultaneous data requests (e.g., training across 100+ GPUs)
• Optimize checkpointing (saving model progress) without slowing workflows

examples:

• WEKA: Acts like a “highway” for data, ensuring GPUs stay busy (making it up to 20x “faster” by feeding it enough data to match the processing power)
• Lustre: Older but reliable for large-scale projects; slower with small files

use case: Choose these if you’re running complex models like image generators or LLMs.

2. object storage (for massive datasets)

what it does:

• Stores petabytes of raw data (images, logs, sensor data) cheaply
• Scales easily but isn’t ideal for real-time processing

examples:

• AWS S3: Cloud-based, pay-as-you-go
• Ceph: Open-source option for on-prem setups

use case : Start here if you’re collecting data from IoT devices or building a training dataset.

3. hybrid solutions (for mixed workloads)

what they do:

• Combine speed with affordability
• Support multiple protocols (NFS, S3) for flexibility

examples:

• VAST Data: Uses QLC SSDs for up to 80% lower cost than traditional flash
• Pure Storage FlashBlade: offering elastic scaling of compute and capacity, protocol support for NFS, SMB, and S3, and features like secure multi-tenancy, inline compression, and erasure coding for resilience

use case : Opt for these if you’re handling both AI training and everyday analytics.

4-step framework for choosing a storage:

1. map your workflow

• Training: Prioritize speed (NVMe/parallel file systems)
• Inference: Balance speed and cost (hybrid SSD/HDD)
• Archiving: Use object storage or tape backups

2. calculate your needs

metric formula example:

throughput (datasetsize) /training time 1PB / 24h = ~11.6 GB/s

storage capacity raw data + 30% for intermediate 100TB → 130TB

3. avoid common pitfalls

• mistake: Using consumer-grade SSDs for enterprise workloads (they wear out fast).
• fix:  Deploy data center-grade SSDs with higher endurance.
• mistake: Storing all data on an expensive flash.
• fix: Tier storage (hot → warm → cold) using automation tools.

4. test before scaling

Run benchmarks with tools like:

• FIO: Measures IOPS/latency
• MLPerf Storage: Simulates real AI workloads

future-proofing your storage:

1. AI-Driven Optimization & automations : AIOps includes automation, performance management and service management and automates many of the decisions involved in scaling and securing storage systems.
2. New systems like VAST’s Nvidia-powered storage use machine learning to predict data access patterns, automatically moving hot data to faster tiers.
3. Predictive analytics helps optimize storage platforms by anticipating storage needs, preventing bottlenecks, and enhancing data retrieval speeds.

circular material to scale up in a budget-friendly way

Imagine building a modern AI infrastructure without breaking the bank or compromising on performance. Does this sound too good to be true? Not with our circular IT solutions!

While the idea of using circular equipment for high-stakes AI projects might raise eyebrows, it’s a game-changer when done right. Here’s the secret sauce:

1. rigorous quality control: Every piece of hardware undergoes meticulous testing to ensure it performs like new (or even better).
2. peace of mind: We offer full warranties for our circular solutions, eliminating the risk factor.
3. flexibility is king: Say goodbye to vendor lock-in. Mix and match components to create your ideal setup.
4. always available: No more waiting months for back-ordered parts. Get what you need when you need it.
5. budget-friendly power: Access top-tier hardware at a fraction of the cost, stretching your AI budget further.

When you factor in sustainability, circular solutions are the ultimate way of future-proofing your infrastructure!

conclusion

As we wrap up our small AI storage guide, it’s clear that choosing the right solution is crucial for unleashing the full potential of your AI projects. By understanding the unique demands of AI workloads and following our 4-step framework, you’re now equipped to make informed decisions that balance performance, scalability, and cost-effectiveness.

Remember, the landscape of AI storage is rapidly evolving. From high-speed file systems that keep your GPUs humming to object storage solutions that tame massive datasets and hybrid options that offer the best of both worlds, there’s a solution tailored to your specific needs. As AI continues to transform industries, staying ahead of the curve with optimized storage infrastructure will be a key differentiator for successful IT teams. Circular IT solutions offer an innovative approach to building a robust AI infrastructure without breaking the bank. By leveraging rigorously tested and warrantied circular hardware, you can access top-tier performance at a fraction of the cost, all while contributing to a more sustainable tech ecosystem.

Don’t be afraid to test, iterate, and adapt your storage strategy as your AI initiatives grow. With the right foundation in place, you’ll be well-positioned to tackle the exciting challenges and opportunities that lie ahead in the world of artificial intelligence. Let’s get in touch today if you need any guidance or hardware; we are always happy to help!