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GPU Servers · AI · HPC · Rendering

Enterprise GPU Servers for AI and HPC.

Deploy dedicated GPU server infrastructure for artificial intelligence, machine learning, model training, inference, scientific computing, rendering and other compute-intensive workloads.

Dedicated GPU GPU resources reserved for your workloads.
High Memory Memory sized for large models and datasets.
NVMe Storage Fast local storage for data-heavy pipelines.
High-Speed Network Connectivity for data transfer and clusters.
Single & Multi-GPU Training & Inference Rendering Workloads Custom Configurations
GPU Server Request CUSTOM CONFIGURATION

Build Your GPU Infrastructure

Share your workload, GPU, memory and storage needs. Our infrastructure team will recommend a GPU server configuration for it.

Optional — details help our team size the right configuration. 0 / 1500
Secure submission Infrastructure team review Custom GPU sizing
Why GPU compute

Parallel Compute for Demanding Workloads

GPUs execute large numbers of parallel operations, which makes GPU servers well suited to machine learning, deep learning, scientific computing, simulation, rendering and data processing.

HostGraber GPU servers run these workloads on dedicated hardware, with memory, storage and networking configured around your models, datasets and software stack.

  • Parallel processing

    Large workloads across thousands of GPU cores

  • Accelerated AI

    Faster machine learning and deep learning

  • High memory

    For memory-intensive AI and data workloads

  • Dedicated hardware

    Resources reserved for your computations

GPU infrastructure architectureAI and HPC applications run on dedicated GPU servers, backed by high-speed NVMe storage for models and datasets and a private network for multi-node environments. Typical workloads include training, inference, HPC and rendering.APPLICATIONAI / HPCYour workloadCOMPUTEGPU serverDedicated GPUsDATANVMe storageModels & datasetsNETWORKPrivate LANMulti-node linksACCELERATED WORKLOADSTRAININGModelsINFERENCEServingHPCSimulationRENDERING3D & videoSingle GPUMulti-node clusters
Illustrative. Multi-node GPU environments are designed around compute, memory, storage, networking and workload requirements.
GPU server solutions

GPU Infrastructure for Every Stage of AI

From a single GPU for development to multi-GPU servers for training, every configuration is built around the workload it runs.

Single GPU server

Dedicated GPU infrastructure for development, inference and smaller workloads.

Multi-GPU server

Multiple GPUs in one server for larger accelerated workloads.

AI training server

High-memory GPU configurations for model training and experimentation.

AI inference server

Infrastructure built around AI inference and application serving.

GPU rendering server

Accelerated compute for rendering, CAD, video and visual workloads.

Custom GPU infrastructure

CPU, GPU, memory, storage and network designed around your workload.

Request GPU Server
GPU infrastructure

More Than a GPU: A Complete Platform

GPU performance depends on everything around it. Memory, storage and networking are planned together with the GPUs.

  • Dedicated GPU resources

    Predictable performance for your workloads.

  • High memory configurations

    For AI and compute-intensive workloads.

  • Fast NVMe storage

    For datasets, models and application files.

  • High-speed networking

    For data-intensive GPU workloads.

  • Private networks

    Connect GPU, application and storage nodes. Private network

  • Custom architecture

    Built around your software stack.

Compute comparison

GPU vs CPU Compute

CPU servers excel at general applications. GPU servers are built for parallel workloads such as AI, HPC, data and rendering.

CapabilityCPU serverGPU server
General applicationsExcellentSupported
Parallel processingLimitedOptimised
AI trainingSlowerAccelerated
RenderingGeneralAccelerated
Best forTraditional workloadsAI, HPC, data and rendering

Need CPU compute for your application tier? Explore dedicated servers →

Technical configuration

Configured Around Your Workload

Every GPU server is specified around the models, datasets and software it will run.

GPU

Single GPU, multi-GPU and custom GPU configurations

CPU

High-core enterprise processors

Memory

High-capacity ECC memory options

Storage

NVMe, SSD and high-capacity storage options

Network

High-speed network connectivity

IP

IPv4 / IPv6 options

OS

Linux and supported custom environments

Architecture

Customised according to workload

Configure Your GPU ServerDiscuss on WhatsApp
Why HostGraber

GPU Infrastructure From One Partner

Build GPU infrastructure alongside dedicated servers, private networking, storage, monitoring and managed services, through a single infrastructure partner.

Talk to Our Infrastructure Team
  • Custom GPU configurations
  • High-memory server options
  • High-speed NVMe storage
  • Private network architecture
  • Monitoring and server management
  • Infrastructure consulting
  • Dedicated hardware
  • Single and multi-GPU
  • Built around your stack
Accelerated workloads

What You Can Run on GPU Servers

  1. 01

    Artificial intelligence

    Develop and deploy AI-powered applications and models.

  2. 02

    Machine learning

    Accelerate training, experimentation and inference.

  3. 03

    Generative AI and LLMs

    Fine-tuning, inference and AI application workloads.

  4. 04

    High performance computing

    Simulations, scientific and parallel computing.

  5. 05

    3D rendering

    Rendering pipelines for animation and visualisation.

  6. 06

    Video processing

    Encoding, transcoding and media processing.

  7. 07

    Data analytics

    Process large datasets with GPU acceleration.

  8. 08

    Custom GPU workloads

    Infrastructure for specialised compute requirements.

Deployment process

From Workload Review to Go-Live

  1. 01REVIEW

    Workload review

    We understand your models, datasets, applications and compute needs.

  2. 02DESIGN

    Configuration design

    GPU, CPU, memory, storage and networking are selected.

  3. 03PROVISION

    Provisioning

    The approved GPU configuration is deployed.

  4. 04SETUP

    Environment setup

    The supported OS and network environment is prepared.

  5. 05LAUNCH

    Go-live

    Workloads launch with infrastructure support available.

Start Your Workload Review
GPU server insights

Planning GPU Infrastructure: Training, Inference and Scale

An overview for AI, data and engineering teams: how training and inference needs differ, how to size memory and storage, and when to move to multi-GPU or multi-node designs.

Training vs inference

Training and inference place different demands on infrastructure. Identifying which one dominates your workload is the first step in choosing a configuration.

AspectTrainingInference
PurposeBuild or fine-tune a modelServe predictions from a trained model
Workload patternLong, intensive jobsContinuous, request-driven
Key priorityGPU count and GPU memoryResponsiveness and availability
Typical setupHigh-memory, often multi-GPUSingle or multiple GPUs, sized to demand

Sizing memory and storage

GPU memory determines the size of model that can be loaded, while system memory and storage determine how quickly data reaches the GPU. Large datasets benefit from fast NVMe storage close to the compute, with high-capacity storage for archives and checkpoints.

Where datasets are very large, GPU servers can be connected to separate high-performance or high-capacity storage servers.

Multi-GPU and multi-node

Workloads that outgrow a single GPU can move to multi-GPU servers, subject to hardware availability, chassis capacity and workload requirements. Beyond one server, multi-node environments connect GPU, application and storage nodes over private networking.

Who uses GPU servers

GPU servers are used by AI startups, machine learning teams, SaaS companies, research organisations, universities, media studios, engineering companies and data analytics teams.

Preparing a GPU server request

The following details help our team recommend the right configuration:

  • Workload type: training, inference, HPC, rendering or analytics
  • Frameworks, model size and dataset size
  • Number of GPUs and memory required, if known
  • Storage capacity and performance needs
  • Networking, private network or colocation requirements

Share these details through the GPU server request form and our team will recommend a configuration.

GPU glossary

Key GPU Computing Terminology

GPU
Graphics processing unit: a processor built for large numbers of parallel operations.
GPU memory (VRAM)
Memory on the GPU itself, which limits the size of model or data it can hold.
Model training
Teaching a machine learning model from data, typically the most compute-intensive stage.
Inference
Using a trained model to produce predictions or outputs.
Fine-tuning
Adapting an existing model to a specific task with additional training.
LLM
Large language model: a generative AI model trained on large volumes of text.
HPC
High performance computing: large-scale simulation and scientific computation.
Multi-GPU
A server or cluster that uses more than one GPU for a workload.
NVMe
A high-speed storage interface suited to large datasets and model files.
ECC memory
Error-correcting memory that detects and corrects certain data errors.
Frequently asked questions

GPU Server Questions Answered

Have a question about your workload? Share your requirements and our infrastructure team will advise.

Ask Our Infrastructure Team
What is a GPU server?

A GPU server combines traditional server processors with one or more graphics processing units for accelerated parallel computing workloads.

What workloads are GPU servers suitable for?

GPU servers are commonly used for AI, machine learning, deep learning, inference, HPC, rendering, video processing and data analytics.

Can I get multiple GPUs in one server?

Multi-GPU configurations can be designed depending on hardware availability, chassis capacity and workload requirements.

Can I use GPU servers for AI model training?

Yes. GPU servers are commonly used to accelerate machine learning and deep learning model training.

Can GPU servers be connected to dedicated storage?

Yes. GPU environments can be designed with separate high-performance or high-capacity storage infrastructure.

Do you provide private networking between GPU servers?

Private networking can be included for supported multi-node GPU and storage architectures.

Can you build a custom GPU configuration?

Yes. GPU, CPU, memory, storage and networking can be planned according to workload and infrastructure requirements.

Which GPU models are available?

GPU models are confirmed according to hardware availability and workload requirements. Share your workload and our team will recommend a suitable configuration.

Can GPU servers be monitored and managed?

Yes. Monitoring and server management can be added to GPU deployments.

How do I get started with a GPU server?

Complete the GPU server request form with your workload type and requirements. Our infrastructure team will review them and recommend a configuration.

Enterprise GPU servers

Built for AI. HPC. Rendering. Data.

Tell us about your workload. Our infrastructure team will recommend a GPU server configuration built around it.

Request GPU Server Quote Discuss on WhatsApp
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