Datanet Hosting provides high-performance HPC servers for AI, machine learning, scientific research, data analytics and compute-intensive workloads.
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Power demanding workloads with compute infrastructure designed for parallel processing, accelerated computing and data-intensive applications. HPC platforms combine powerful processors, accelerator options, high-speed interconnects and scalable storage to support complex jobs.
Choose an HPC configuration based on your application stack, parallelization model, accelerator requirements, memory footprint, storage throughput and inter-node communication needs.
Run parallel scientific and engineering workloads on multi-core server platforms with high memory capacity and balanced compute resources.
Accelerate AI training, deep learning, simulation and visualization with compatible GPU platforms designed for compute-intensive tasks.
Feed compute nodes with fast, reliable access to datasets, checkpoints and project files using high-capacity and high-throughput storage.
Build multi-node environments for distributed processing, workload scheduling and parallel applications across interconnected compute nodes.
Support research, computational engineering, rendering and AI development with high-performance CPU/GPU resources matched to application needs.
Design a compute platform around software requirements, CPU/GPU ratios, memory bandwidth, storage throughput, network topology and scaling plans.
Combine compute, accelerator, memory, storage and networking technologies selected for your HPC software and workload profile.
HPC systems are engineered to process complex calculations and large datasets using parallel computing, accelerator hardware and fast node-to-node communication. They help research institutions and enterprises reduce processing time for workloads that exceed general-purpose servers.
Distribute complex jobs across multiple CPU cores, GPUs or compute nodes to execute suitable workloads in parallel.
Use GPU accelerators and optimized compute libraries for AI, deep learning, scientific computing and simulation workloads.
Scale compute nodes, high-bandwidth memory, fast storage and interconnects to meet evolving performance and capacity requirements.
Run demanding workloads that require high compute density, parallel processing, accelerator performance and high-throughput data movement.
Execute computational fluid dynamics, molecular modelling, weather modelling and other scientific simulations on parallel compute platforms.
Train and run machine learning models using GPU acceleration, large memory capacity and fast access to training datasets.
Process large datasets and complex analytical workloads with scalable compute resources and high-throughput storage.
Support engineering workloads, 3D rendering, visualization and computational design with suitable CPU and GPU resources.
Select hardware according to application benchmarks, parallel processing model, memory footprint, data throughput and target completion times.
Choose high-core-count CPUs and compatible GPUs or accelerators based on software support, precision requirements and compute intensity.
Plan ECC memory capacity and bandwidth for large datasets, simulation meshes, in-memory analytics and multi-GPU workloads.
Combine NVMe scratch storage, shared datasets and capacity tiers to support high IOPS, sequential throughput and checkpoint operations.
Select high-bandwidth Ethernet or InfiniBand networking for low-latency communication, distributed training and multi-node jobs.
Plan secure user access, workload scheduling, monitoring, orchestration and administration across compute nodes.
Get assistance with platform selection, OS and driver setup, cluster configuration, workload validation and performance tuning.
We assess your compute workloads, software stack and performance objectives, then plan an HPC architecture and deployment approach.
Review application requirements, parallelization, dataset size, memory demand, accelerator support and expected job throughput.
Define CPU/GPU nodes, memory, storage tiers, interconnect topology and cluster capacity based on workload benchmarks and growth.
Configure server hardware, operating systems, GPU drivers, high-speed networking, cluster management and workload scheduling software.
Validate workloads, monitor performance and document operations before arranging ongoing administration and optimization support.
An HPC server is a compute platform designed to process complex, compute-intensive workloads using high-performance CPUs, GPUs, large memory capacity and fast data movement. HPC systems may operate as standalone nodes or as part of a larger cluster.
Common workloads include scientific simulations, computational engineering, AI and machine learning, deep learning, big data analytics, rendering, research computing and other applications that can benefit from parallel processing.
CPU-based systems are suited to a broad range of general-purpose and parallel tasks. GPUs can accelerate highly parallel workloads such as supported AI, deep learning, simulation and visualization applications. The right design depends on software compatibility and workload characteristics.
Yes. Configurations with compatible GPUs, sufficient memory, fast storage and suitable software drivers can support AI model development, training and inference. GPU memory and framework compatibility should be assessed before selecting hardware.
High-bandwidth, low-latency networking enables compute nodes to exchange data efficiently during distributed jobs. Ethernet or InfiniBand options should be selected according to application communication patterns, scale and performance targets.
Memory requirements depend on dataset size, simulation models, application processes and the number of concurrent jobs. HPC configurations commonly use ECC memory; capacity and bandwidth should be sized using workload profiling or application guidance.
NVMe SSDs are useful for fast local scratch space, while shared or parallel storage can serve datasets across multiple nodes. The storage design should consider throughput, IOPS, capacity, data protection and checkpoint requirements.
Many HPC environments use Linux distributions and workload management tools, along with compilers, MPI libraries, GPU drivers and application-specific software. Confirm hardware, driver, operating system and software compatibility for the intended environment.
HPC environments can be designed to expand through additional compute nodes, accelerators, memory, storage and networking. Cluster topology, software licensing, power, cooling and application scaling should be considered in the expansion plan.
Submit the consultation form with details about your applications, software stack, CPU and GPU requirements, memory footprint, dataset size, storage throughput and performance objectives. The Datanet Hosting team can review the requirements and discuss a suitable HPC configuration.
