Assemblage, Expédition et Support de toutes les commandes depuis l'Union Européenne
Assemblage, Expédition et Support de toutes les commandes depuis l'Union Européenne
Built for AI Training, HPC, and Distributed AI Workloads
Broadberry designs AI networking infrastructure for environments where GPUs, storage, and distributed systems must communicate at high speed and low latency.
AI workloads place different demands on networking than traditional enterprise applications. Large-scale AI training, distributed inference, and GPU clusters require high-throughput data movement, fast inter-node communication, and predictable scalability across infrastructure.
From AI PODs to AI factories, Broadberry designs networking architectures tailored to workload requirements, cluster scale, and performance goals. As an NVIDIA Elite Partner, Broadberry is accredited to design and build AI infrastructure including AI PODs and AI factories for enterprise AI and HPC environments.
Contact us to discuss AI Networking
NVIDIA Spectrum-2 based 25GbE/100GbE 1U Open Ethernet switch with Cumulus Linux, 48 SFP28 ports and 12 QSFP28
NVIDIA Spectrum-2 based 25GbE/100GbE 1U Open Ethernet switch with Cumulus Linux, 48 SFP28 ports and 12 QSFP28
NVIDIA Spectrum-3 based 100GbE 2U Open Ethernet switch with Cumulus Linux, 64 QSFP28 ports, 2 Power Supplies (AC), x86 CPU, standard depth, C2P airflow, Rail Kit
NVIDIA Spectrum-3 based 100GbE 2U Open Ethernet switch with Cumulus Linux, 64 QSFP28 ports, 2 Power Supplies (AC), x86 CPU, standard depth, P2C airflow, Rail Kit
NVIDIA Quantum 2 based NDR InfiniBand Switch, 64 NDR ports, 32 OSFP ports, 2 Power Supplies (AC), Standard depth, Unmanaged, P2C airflow, Rail Kit
NVIDIA Quantum 2 based NDR InfiniBand Switch, 64 NDR ports, 32 OSFP ports, 2 Power Supplies (AC), Standard depth, Unmanaged, P2C airflow, Rail Kit
NVIDIA Quantum 2 based NDR InfiniBand Switch, 64 NDR ports, 32 OSFP ports, 2 Power Supplies (AC), Standard depth, Managed, C2P airflow, Rail Kit
NVIDIA Quantum 2 based NDR InfiniBand Switch, 64 NDR ports, 32 OSFP ports, 2 Power Supplies (AC), Standard depth, Managed, P2C airflow, Rail Kit
NVIDIA Spectrum-3 based 400GbE 1U Open Ethernet Switch with Cumulus Linux, 32 QSFPDD ports, 2 Power Supplies (AC), x86 CPU, standard depth, C2P airflow, Rail Kit
NVIDIA Spectrum-3 based 400GbE 1U Open Ethernet Switch with Cumulus Linux, 32 QSFPDD ports, 2 Power Supplies (AC), x86 CPU, standard depth, P2C airflow, Rail Kit
NVIDIA Spectrum-4 based 400GbE 2U Open Ethernet switch with Cumulus Linux Authentication, 64 QSFP56-DD ports and 2 SFP28 ports, 2 power supplies (AC), x86 CPU, Secure-boot, standard depth, Power-to-Connector airflow, Tool-less Rail Kit
NVIDIA Spectrum-4 based 400GbE 2U Open Ethernet switch with Cumulus Linux Authentication, 64 QSFP56-DD ports and 2 SFP28 ports, 2 power supplies (AC), x86 CPU, Secure-boot, standard depth, Connector-to-Power airflow, Tool-less Rail Kit
NVIDIA Spectrum-4 based 800GbE 2U Open Ethernet switch with Cumulus Linux Authentication, 64 OSFP ports and 1 SFP28 port, MGX Mount with Busbar, x86 CPU, Secure-boot, standard depth, Connector-to-Power Airflow, Tool-less Rail Kit
NVIDIA Spectrum-4 based 800GbE 2U Open Ethernet switch with Cumulus Linux Authentication, 64 OSFP ports and 2 SFP28 ports, 4 AC PSUs, Secure-boot, standard depth, Connector-to-Power Airflow, Tool-less Rail Kit
AI networking refers to the high-performance interconnects and network architectures that enable communication between GPUs, servers, storage systems, and edge environments.
Unlike traditional enterprise networking, AI networking must support:
Performance depends on how compute, storage, and networking operate together as a unified system. When networking becomes a bottleneck, even the most powerful GPUs and accelerators cannot operate at peak efficiency.
Why does networking matter for AI workloads?
AI workloads rely on rapid data movement between GPUs, storage systems, and compute nodes. Poor network performance can leave GPUs waiting for data or communication between nodes, slowing AI training and inference workloads.
What is the difference between AI networking and traditional enterprise networking?
AI networking prioritises low latency, high throughput, and distributed communication between systems, while traditional enterprise networking is typically designed for transactional business applications.
What causes networking bottlenecks in AI environments?
Common bottlenecks include insufficient bandwidth, network congestion, slow inter-node communication, and poor scaling across distributed GPU environments.
Modern AI workloads rarely run on a single system. Instead, they rely on clusters of GPUs, TPUs, and other accelerators distributed across multiple servers. These environments require continuous, high-speed communication between nodes to support large-scale AI training and parallel processing.
Industries such as healthcare, industrial automation, autonomous systems, IoT analytics, and financial services require AI systems capable of real-time responsiveness, where network performance directly affects outcomes.
AI deployments increasingly span hybrid environments that combine on-premise infrastructure, cloud platforms, and edge devices. High-performance networking enables these environments to operate together efficiently.
AI models continue to grow in size and complexity, increasing demands on network bandwidth, latency, and scalability. Networking architectures must be able to expand without becoming a bottleneck.
Broadberry delivers AI networking infrastructure as part of fully integrated AI, HPC and GPU-accelerated environments.
Capabilities include:
Broadberry works with customers to evaluate workload behaviour, scalability requirements, and performance goals to determine the most appropriate networking architecture for their environment.
What is InfiniBand used for?
InfiniBand is commonly used in AI training and HPC environments that require ultra-low latency, high bandwidth, and fast communication between GPU clusters and compute nodes.
What networking is required for AI training?
AI training environments typically require high-bandwidth, low-latency networking capable of supporting distributed GPU communication, parallel processing, and fast data movement between compute and storage systems.
Can networking limit GPU performance?
Yes. Insufficient bandwidth or high network latency can leave GPUs waiting for data or communication between nodes, reducing overall training efficiency and system performance.
What is network latency?
Network latency refers to the time it takes for data to travel between systems or nodes. Low latency is critical for distributed AI workloads that rely on constant communication between GPUs and servers.
What is the difference between NVLink and InfiniBand?
NVLink is a high-speed GPU-to-GPU interconnect used within tightly coupled systems, while InfiniBand is a network fabric designed for communication across multiple servers and distributed AI clusters.
How much bandwidth does AI training require?
Bandwidth requirements depend on model size, dataset scale, and the number of GPUs involved. Large-scale AI training environments often require 100GbE, 200GbE, 400GbE, or InfiniBand networking.
When should AI networking scale out?
AI networking should scale out when GPU clusters, datasets, or distributed workloads grow beyond the capabilities of a single system or network fabric.
What is the role of networking in an AI factory?
Networking connects GPUs, storage, and compute resources across the AI factory, enabling high-speed data movement, distributed training, and efficient scaling of AI workloads.
Should AI networking run on Ethernet or InfiniBand?
The right choice depends on workload requirements, performance goals, scalability needs, and budget. InfiniBand is commonly used for large-scale AI training and HPC environments, while Ethernet is often used for more flexible or mixed infrastructure deployments.
Broadberry works with customers to evaluate these trade-offs and determine the most appropriate networking architecture for their AI environment.
Notre Procédure de Tests rigoureuseAvant de quitter nos ateliers, toutes les solutions de serveur et de stockage Broadberry sont soumises à une procédure de test rigoureuse de 48 heures. Ceci, associé à un choix de composants de haute qualité, garantit que toutes nos serveurs et solutions de stockage répondent aux normes de qualité les plus strictes qui nous sont imposées.
Une Flexibilité InégaléeNotre principal objectif est d'offrir des serveurs et des solutions de stockage de la plus haute qualité. Nous comprenons que chaque entreprise a des exigences différentes et sommes en mesure d'offrir une flexibilité inégalée dans la personnalisation et la conception de serveurs et de solutions de stockage.
Nous nous sommes imposés comme un incontournable fournisseur de stockage en Europe et fournissons depuis 1989 nos solutions de serveurs et de stockage aux plus grandes marques mondiales. Quelques exemples de clients :
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