Scope and technical decisions
Specify accelerator memory and compute needs, host CPU and RAM, local dataset and checkpoint storage, high-speed networking, cluster scale, framework compatibility and security boundaries.
AI & GPU Servers
GPU-accelerated servers and workstations for machine learning, inference, rendering and other compute-intensive workloads.
Key features
- Multi-GPU server and workstation configurations
- Specified against model size, training and inference needs
- High-throughput storage and networking to feed the GPUs
- Power and cooling requirements assessed before installation
- On-premise alternative to rented cloud compute
Applications
- Machine learning training and inference
- Data science and analytics
- Rendering and simulation
- On-premise AI platforms
Brands available for procurement
- NVIDIA
- Dell EMC
- HPE
- Supermicro
- Lenovo
- AMD
Brands are listed to describe procurement scope. Duara Group does not claim authorised reseller status, partnership or distribution rights unless stated in writing. Availability and specification are confirmed per inquiry.
Planning AI & GPU Servers
AI servers must be sized to training, fine-tuning or inference workloads, model size, precision, batch behavior and concurrency. GPU count alone is not an architecture.
Deployment and acceptance
Validate rack depth and weight, power circuits, cooling, network fabric, drivers, firmware, scheduler or container stack, data access and representative workload tests.
Operations, maintenance and support
Monitor accelerator utilisation, thermals, power, storage and network bottlenecks. Plan driver and framework compatibility, capacity growth, spares and workload isolation.
What to send for a scoped quotation
Share models and workload type, dataset and checkpoint sizes, training or inference targets, concurrency, framework, GPU memory need, cluster plan, power, cooling, rack and support requirements.
Industries served
- Corporate and enterprise
- Government and public sector
- NGOs and development programmes
- Healthcare organisations
- Education institutions
- Retail and manufacturing
Duara Technology at a glance
Duara Technology delivers end-to-end technology solutions for businesses, governments, NGOs, healthcare organisations, educational institutions, retailers, manufacturers and other enterprise clients - from hardware and networks through to software, cloud and payment platforms. Supply is inquiry-based: specification, quantity and deployment requirements are confirmed before quotation.
Frequently asked questions
Who is AI & GPU Servers suitable for?
AI servers must be sized to training, fine-tuning or inference workloads, model size, precision, batch behavior and concurrency. GPU count alone is not an architecture.
What technical information is needed to plan AI & GPU Servers?
Specify accelerator memory and compute needs, host CPU and RAM, local dataset and checkpoint storage, high-speed networking, cluster scale, framework compatibility and security boundaries.
What should deployment and handover cover?
Validate rack depth and weight, power circuits, cooling, network fabric, drivers, firmware, scheduler or container stack, data access and representative workload tests.
What information is needed for a quotation?
Share models and workload type, dataset and checkpoint sizes, training or inference targets, concurrency, framework, GPU memory need, cluster plan, power, cooling, rack and support requirements.
Related technology
Request AI & GPU Servers
Tell us the specification, quantity and destination and our sourcing team will come back with availability and a quotation.