AI Storage Servers

Solutions de stockage optimises pour lIA

Librez toute la puissance de votre infrastructure GPU

Les charges de travail lies lIA exigent plus que jamais des systmes de stockage. Les pipelines dentranement et dinfrence reposent sur un dbit extrmement lev, une latence ultra-faible, et la capacit alimenter simultanment des milliers de curs GPU en donnes. Les solutions de stockage traditionnelles ne peuvent tout simplement pas suivre, ce qui entrane des GPU inactifs, des goulots dtranglement et des performances imprvisibles.

Les solutions de stockage optimises pour lIA de Broadberry sont conues pour liminer ces contraintes, afin de garantir une utilisation maximale de vos clusters et une monte en charge fluide de vos projets dIA, du dveloppement la production.

Pourquoi le stockage optimis pour lIA est essentiel

Lentranement moderne des modles dIA ncessite :

• un accs parallle massif aux donnes
• des pipelines haute bande passante pour les grands jeux de donnes
• des E/S faible latence pour lentranement distribu
• des oprations rapides de checkpointing et de reprise
• des performances prvisibles grande chelle

Les plateformes de stockage IA de Broadberry sont conues spcifiquement pour rpondre ces exigences. Elles permettent plusieurs nuds de lire et dcrire simultanment sur des jeux de donnes partags, avec une monte en charge linaire mesure que vous ajoutez de la capacit de calcul ou de stockage. Le rsultat : un temps dentranement rduit, des pipelines plus fluides et une utilisation constamment leve des GPU.

Notre portefeuille de solutions de stockage IA

Broadberry propose trois technologies de stockage de pointe, chacune valide pour les environnements IA haute performance et certifie pour les architectures NVIDIA SuperPOD.

WekaIO Stockage de fichiers haute performance (Certifi NVIDIA SuperPOD)

WekaIO fournit un systme de fichiers de nouvelle gnration, faible latence, spcialement conu pour lIA et le calcul acclr. En tant que solution certifie NVIDIA SuperPOD, il sintgre parfaitement aux architectures de rfrence IA valides.

Principaux avantages :

• dbit extrmement lev et latence ultra-faible
• ingestion rapide des jeux de donnes et checkpointing acclr
• lectures et critures partages hautement concurrentes
• performances prvisibles mesure que les clusters grandissent
• meilleure utilisation des GPU et cycles dentranement plus courts

WekaIO est idal pour les organisations qui excutent des pipelines dentranement grande chelle, des charges de travail multinuds et des clusters GPU forte densit.

DDN Storage Des performances extrmes pour lIA grande chelle (Certifi NVIDIA SuperPOD)

DDN est un leader mondial du stockage haute performance, conu pour les environnements IA et HPC les plus exigeants. Certifi pour NVIDIA SuperPOD, DDN offre une base prouve pour les dploiements GPU de niveau entreprise.

Principaux avantages :

• dbit parallle soutenu avec une latence constante
• ingestion acclre des donnes et accs partag aux jeux de donnes
• checkpointing haute frquence sans goulots dtranglement
• rduction des blocages dans les pipelines et excution plus rapide des tches
• performances valides, reproductibles et volutives

DAOS Stockage scale-out de nouvelle gnration pour lIA et le HPC

DAOS (Distributed Asynchronous Object Storage) est une architecture scale-out de pointe, conue pour les charges de travail qui exigent une bande passante extrme et un minimum de surcharge. Reposant sur NVMe et sur un modle dobjets distribu, DAOS excelle dans les environnements forte concurrence et aux E/S parallles grande chelle.

Idal pour :

• le stockage scratch et de staging haute vitesse
• lentranement distribu trs grande chelle
• les pipelines intensifs en donnes avec un haut niveau de paralllisme
• les environnements HPC/IA ncessitant un dbit maximal

DAOS est le choix idal pour les clients la recherche de performances et dune volutivit sans compromis.

Pourquoi Broadberry ?

Depuis plus de 37 ans, Broadberry fournit des infrastructures haute performance aux organisations les plus exigeantes au monde, des grandes universits aux entreprises internationales, en passant par les agences gouvernementales et les instituts de recherche.

Ce qui nous distingue :

• une expertise approfondie en IA, HPC et systmes acclrs par GPU
• des solutions sur mesure, conues en fonction de vos charges de travail
• un support mondial et une disponibilit long terme des composants
• une fiabilit prouve dans des environnements critiques

Que vous construisiez votre premier cluster IA ou que vous passiez une vritable usine IA, Broadberry vous fournit la base de stockage ncessaire pour maintenir vos GPU leur niveau de performance maximal.

What is AI Storage?

AI-optimised storage systems are designed to support distributed AI workloads, parallel data access, and high-throughput pipelines.

Unlike traditional storage environments, AI storage must support:

• Massive parallel data access
• High-throughput data pipelines
• Low-latency I/O
• Fast checkpointing and recovery
• Shared access across multiple compute nodes

Why AI-Optimised Storage Matters

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 vs Traditional Storage

WekaIO (NVIDIA SuperPOD Certified)

AI-Optimised File Storage for AI Training and GPU Environments

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.

DDN (NVIDIA SuperPOD Certified)

Enterprise AI Storage for Large Scale Training and HPC

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.

DAOS

Scale-Out Storage for Extreme Concurrency and Data Movement

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.

Workloads Supported

These AI-optimised storage platforms support:

• Large language model (LLM) training
• AI model development
• Computer vision pipelines
• Distributed AI training
• HPC environments
• • Data analytics workloads

Who These Systems Are For

These environments often require scalable AI storage infrastructure capable of supporting large datasets, distributed training, and high-throughput data movement.

AI storage servers are typically used by:

• Research institutions
• Enterprises building AI infrastructure
• GPU cluster environments
• AI factories
• HPC organisations

Why Broadberry?

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

AI Storage FAQ

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.