AI performance is frequently limited by data movement rather than compute capacity.
Common bottlenecks include:
- Slow dataset loading
- Storage contention between nodes
- Inefficient checkpointing
- Insufficient throughput
AI-optimised storage helps improve:
- GPU utilisation
- Time to train
- Training consistency
- Pipeline efficiency
Large-scale distributed AI workloads frequently shift bottlenecks from computation to communication and data movement.
| AI Storage | Traditional Enterprise Storage |
| Primary Purpose | Support AI pipelines and distributed workloads | General business applications |
| Access Pattern | High concurrency, shared access | Transactional and sequential |
| Compute Requirements | Very high, often distributed across multiple GPUs | Moderate to high, depending on workload |
| Performance Focus | Throughput and parallel I/O | Capacity and availability |
| Data Movement | Large datasets, rapid ingest | Moderate data movement |
| Scaling | Scale-out architectures | Often scale-up architectures |
WekaIO delivers a low latency file system designed for AI and accelerated computing workloads. As an NVIDIA SuperPOD certified solution, it integrates seamlessly into validated AI reference architectures.
Best suited for:
- Large-scale AI training pipelines
- Multi-node AI and HPC workloads
- High density GPU clusters
Key benefits:
- Extremely high throughput and ultra-low latency
- Fast dataset ingest and rapid checkpointing
- Highly concurrent shared reads and writes
- Predictable performance as clusters scale
- Improved GPU utilisation and shorter training cycles
DDN provides high-throughput storage infrastructure designed for large-scale AI, HPC, and data-intensive environments. DDN is certified for NVIDIA SuperPOD and is commonly deployed in GPU-accelerated AI infrastructure and large-scale training environments.
Best suited for:
- Enterprise AI deployments
- Large GPU clusters
- HPC environments
- Parallel AI workloads
Key benefits:
- Sustained parallel throughput with consistent latency
- Accelerated data ingest and shared dataset access
- High-frequency checkpointing without bottlenecks
- Reduced pipeline stalls and faster job completion
- Repeatable, validated performance at scale
DAOS (Distributed Asynchronous Object Storage) is a scale-out storage architecture designed for AI and HPC workloads that require extreme bandwidth, low overhead, and high levels of parallel I/O. Built on NVMe and a distributed object storage model, DAOS is optimised for environments with high concurrency and large-scale data movement.
Best suited for:
- Fast scratch and staging storage
- Distributed training at massive scale
- Data intensive pipelines with high parallelism
- HPC/AI environments requiring maximum throughput
Key benefits:
- NVMe-native architecture optimised for high-speed storage access
- High parallel I/O performance across distributed environments
- Scale-out design for large AI and HPC workloads
- Efficient support for high-concurrency data pipelines
- Reduced storage bottlenecks in data-intensive workflows
For over 37 years, Broadberry has delivered high-performance infrastructure to universities, enterprises, government agencies, and research institutions worldwide.
Broadberry AI-optimised storage is designed to support the demands of AI training, distributed compute, and GPU-accelerated environments, with solutions tailored to specific workload and performance requirements.
Broadberry works with customers to evaluate throughput, concurrency, scalability, checkpointing, and deployment requirements to determine the most appropriate storage platform for their environment.
What sets us apart
- Deep expertise in AI, HPC, and GPU-accelerated infrastructure
- Custom-engineered storage solutions based on workload requirements
- Global support and long-term component availability
- Proven reliability across production and mission-critical environments
What storage is best for AI training?
AI training environments typically require high-throughput, parallel storage capable of supporting multiple GPUs and compute nodes simultaneously. The best storage architecture depends on dataset size, training scale, concurrency requirements, and performance goals.
Broadberry works with customers to evaluate these factors and recommend storage platforms aligned to real AI workload requirements.
What is checkpointing in AI?
Checkpointing is the process of saving a model’s state during training so work can resume if training is interrupted. Fast checkpointing reduces downtime and minimizes delays during large-scale AI training workloads.
How much bandwidth does AI storage require?
Bandwidth requirements depend on the number of GPUs, dataset size, and workload intensity. Large AI training environments often require high-throughput storage capable of supporting parallel access across multiple nodes.
Insufficient bandwidth can prevent GPUs from receiving data efficiently, reducing overall system utilisation.
What is parallel file storage?
Parallel file storage allows multiple systems or nodes to access and process data simultaneously. This improves throughput and enables distributed AI training and HPC workloads to scale efficiently.
Can storage limit GPU performance?
Yes. Storage bottlenecks can reduce GPU utilisation by delaying data delivery to compute resources. Slow storage, insufficient throughput, or poor data distribution can significantly impact AI training performance.
When should AI storage scale out?
Scale-out storage architectures are beneficial when workloads, datasets, or GPU clusters grow beyond the limits of a single storage system. Scale-out approaches allow capacity and performance to expand incrementally as infrastructure requirements increase.
How do you size AI storage infrastructure?
Storage sizing depends on dataset size, throughput requirements, concurrency levels, checkpoint frequency, retention policies, and overall AI workflow design.
Broadberry works with customers to evaluate these factors and recommend storage architectures aligned to workload performance requirements.
Should AI storage run on-premise or in the cloud?
On-premise AI storage offers greater control over performance, data locality, and long-term cost predictability. Cloud storage provides flexibility and scalability for variable workloads.
Both approaches involve trade-offs between performance, flexibility, scalability, operational control, and long-term cost.
The best approach depends on workload scale, operational requirements, data governance, and infrastructure strategy. Broadberry works with customers to align storage architecture with performance, scalability, operational goals, and budget.
What workloads require AI-optimized storage?
AI-optimized storage is commonly used for:
- Large language model (LLM) training
- Distributed AI training
- Computer vision pipelines
- HPC environments
- Data analytics and simulation workloads
These environments require high-throughput, low-latency access to shared datasets across multiple systems.
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