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Built for Real-Time AI Workloads
Broadberry designs AI inference servers for running trained models in production. These systems support real-time AI applications such as large language models, computer vision, speech processing, and recommendation engines.
Each system is optimised for low-latency response, high request throughput, and consistent performance under load. Deployments can run at the edge, on-premise, or within private cloud environments depending on data, latency, and control requirements.
Broadberry is a NVIDIA Elite partner fully accredited to build AI Infrastructure systems, including AI PODs and AI Factories designed specifically and tailored to specific AI inference workloads.
AI inference is the process of running a trained machine learning model to generate predictions from new data in production environments.
Unlike training, which builds the model, inference focuses on speed, efficiency, and scalability. In production environments, AI inference systems must handle large volumes of requests while maintaining predictable response times.
This makes infrastructure design critical. Performance is not just about compute. It depends on how GPUs, CPUs, memory, storage, and networking work together under real workload conditions.
Broadberry AI inference servers are GPU-accelerated systems designed to support production AI inference deployments.
Typical configurations include:
Systems are configured based on AI inference workload requirements, including model size, concurrency levels, latency targets, and deployment constraints.
What is an AI inference server?
An AI inference server is a system designed to run trained machine learning models in production, generating predictions from new data in real time for AI applications.
What is the difference between AI training and inference?
Training builds and optimises a model using large datasets. Inference uses that trained model to process new inputs and return results quickly and efficiently.
What workloads require AI inference servers?
Common workloads include large language model (LLM) inference, computer vision, natural language processing, speech recognition, and recommendation systems used in production AI environments.
Broadberry AI inference systems are built by balancing compute, storage, power, and form factor based on inference workload requirements. Each component is selected to support throughput, latency, and deployment constraints.
Designed for parallel model execution, enabling high-throughput AI inference workloads across multiple concurrent requests.
Maximise performance per rack unit for data centre deployments where space and power efficiency are critical.
Suitable for lightweight or power-efficient AI inference workloads that do not require full GPU acceleration.
Designed for high-volume, low-power AI inference environments, including edge and distributed deployments.
Reduces model load times and supports fast access to large datasets.
Enable concurrent inference requests without I/O bottlenecks.
Allows systems to manage and retrieve multiple models efficiently in production environments.
Supports dense GPU configurations while maintaining stable performance.
Enables higher compute density and sustained performance in thermally constrained environments.
Enables higher compute density and sustained performance in thermally constrained environments.
Designed for environments with space, power, or environmental constraints.
Support a range of deployment sizes from single-node to scaled environments.
Allow horizontal scaling for high-demand inference workloads.
These systems are designed to support a range of AI inference workloads, including:
Each workload places different demands on compute, memory, and data movement. System configurations are tailored accordingly to avoid bottlenecks and ensure consistent performance.
AI inference systems operate under different constraints than AI model development or training environments.
Key requirements include:
Broadberry systems are designed with these requirements in mind, ensuring reliable performance as workloads scale.
These AI inference systems are typically deployed by:
They are used in environments where latency, data control, and predictable performance matter.
NVIDIA DGX Spark Founders Edition AI Supercomputer. Designed for a development, pre-production and concept that allows developers to test and fine tune AI Code / software stack prior to AI Production.
Short Depth Single AMD EPYC 9005 / 9004 Series Server with 4x GPU Slots, 2x 2.5" Gen4 NVMe Hot-Swappable bays
Single Intel Xeon 6 6900 Series processors, Supports 4x NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs, dual 10Gb/s LAN ports, redundant power supply, 8x 2.5" SATA/SAS hot-swappable bays.
Dual Intel Xeon 6 Series processors, Supports NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs, dual 10Gb/s LAN ports, redundant power supply, 12x 2.5" NVMe/SATA/SAS & 4x SATA/SAS hot-swappable bays.
Dual AMD EPYC 9005 / 9004 Series, Supports up to 4x NVIDIA RTX PRO 6000 Blackwell - 4x 2.5" NVMe/SATA/SAS & 4x SATA/SAS Drives.
Dual AMD EPYC 9005 / 9004 Series 8x GPU Server - 4x 2.5" NVMe/SATA/SAS & 4x SATA/SAS
Dual AMD EPYC 9005 / 9004 Series AI Inference Server, Supports 8x NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs - 12x 2.5" NVMe/SATA/SAS hot-swap drive bays.
Dual Intel Xeon 6 Series processors, Supports 8x Dual slot Gen5 GPUs, dual 10Gb/s LAN ports, redundant power supply, 8x 2.5" NVMe hot-swappable bays.
NVIDIA DGX H200 with 8x NVIDIA H200 141GB SXM5 GPU Server, Dual Intel® Xeon® Platinum Processors, 2TB DDR5 Memory, 2x 1.92TB NVMe M.2 & 8x 3.84TB NVMe SSDs.
CyberServe EPYC EP2-808S G6 with 8x NVIDIA HGX B300 GPUs, Dual Intel Xeon 6 Series Processors, DDR5 Memory, 2x M.2 slots & 8x NVMe Hot swap drive bays
NVIDIA DGX B200 with 8x NVIDIA Blackwell GPUs, Dual Intel® Xeon® Platinum 8570 Processors, 4TB DDR5 Memory, 2x 1.92TB NVMe M.2 & 8x 3.84TB NVMe SSDs.
NVIDIA DGX B300 with 8x NVIDIA Blackwell Ultra SXM GPUs, Dual Intel® Xeon® 6776P Processors, 2TB DDR5 Memory, 2x 1.92TB NVMe M.2 & 8x 3.84TB E1.S NVMe.
NVIDIA DGX GB200 with 72x NVIDIA Blackwell GPUs, Dual Intel® Xeon® Platinum Processors, 4TB DDR5 Memory, 2x 1.92TB NVMe M.2 & 8x 3.84TB NVMe SSDs.
Why are GPUs used for AI inference?
GPUs accelerate parallel processing, allowing AI inference servers to handle multiple requests at once. This improves throughput and reduces response time for real-time applications.
What does low-latency inference mean?
Low latency refers to how quickly a system can return a result after receiving a request. AI inference systems are designed to minimise delay, especially for real-time applications.
When should inference run on-premise instead of in the cloud?
On-premise inference is often preferred when low latency, data privacy, or predictable performance is required, or when workloads are large enough to justify dedicated infrastructure.
How do you size an AI inference server?
Sizing depends on factors such as model size, number of concurrent users, latency targets, and data throughput. GPU type, memory capacity, and storage speed all play a role.
What role does storage play in inference performance?
Fast storage, such as NVMe, reduces model load times and supports high-throughput data access, which is important for maintaining consistent inference performance.
Can inference systems scale horizontally?
Yes. Inference workloads can scale across multiple nodes or servers, allowing systems to handle increased demand by distributing requests.
What industries use AI inference servers?
Industries include financial services, healthcare, retail, media, manufacturing, and research, anywhere real-time data processing and decision-making are required.
Broadberry AI inference servers support all major AI frameworks and runtimes, enabling deployment across edge, on-premise, and cloud environments.
This allows models to move from development to production without changes to existing AI workflows.
Broadberry provides end-to-end support for deploying and operating AI inference infrastructure.
Systems are built and supported for long-term, production AI environments.
AI inference systems are designed to operate efficiently at scale.
This is especially important for high-volume or always-on inference workloads.
Broadberry has over 30 years of experience delivering high-performance infrastructure across global enterprise, research, and government environments.
AI inference servers are configured based on workload requirements, ensuring the right balance of performance, efficiency, and cost over time for long-term AI deployment.
Unser präzises Testing Alle Broadberry Server- und Storage-Lösungen durchlaufen vor dem Versand aus unserem Lagerhaus einen 48-stündigen Testlauf. In Kombination mit diesem Prüfverfahren sowie den hochqualitativen, branchenführenden Komponenten stellen wir sicher, dass all unsere Server- und Storage-Lösungen den strengsten Qualitätsrichtlinien entsprechen, die an uns gestellt werden.
Unübertroffene FlexibilitätUnser Hauptziel ist es, hochwertige Server- und Speicherlösungen zu einem hervorragenden Preis-Leistungs-Verhältnis anzubieten. Wir wissen, dass jedes Unternehmen unterschiedliche Anforderungen hat, und sind daher in der Lage, unübertroffene Flexibilität bei der Gestaltung maßgeschneiderter Server- und Speicherlösungen anzubieten, um die Bedürfnisse unserer Kunden zu erfüllen.
Wir haben uns als einer der größten Storageanbieter im Vereinigten Königreich etabliert und beliefern seit 1989 die weltweit führenden Marken mit unseren Server- und Storagelösungen. Zu unseren Kunden zählen:
Broadberry Data Systems Allows Buyers to Test Drive New NVIDIA GPU-accelerated Servers
Broadberry Data Systems Allows Buyers to Test Drive New NVIDIA GPU-accelerated Servers
London, UK– Jan 26, 2021 – Broadberry Data Systems, one of the U.K.’s leading providers of customised s
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