Power AI workloads with dedicated GPU infrastructure

GPU as a Service gives you access to powerful GPU servers without purchasing and maintaining the physical hardware yourself. Your environment can combine dedicated GPUs with the compute, RAM, storage and networking required by your workload.

This approach can give you:

• Dedicated GPU capacity for predictable performance
• Compute resources configured around your application
• High-performance SSD storage for demanding workloads
• Bandwidth and networking suited to data-intensive applications
• The ability to scale without continually purchasing new hardware

UKDedicated can build dedicated GPU infrastructure around AI, machine learning and other compute-intensive workloads, giving you the resources you need without the upfront hardware investment.

GPU servers are servers equipped with one or more graphics processing units alongside traditional CPU resources. While a processor is designed for a broad range of computing tasks, GPUs can perform many calculations in parallel.

This makes them particularly useful for:

• AI model training
• AI inference
• Data science and analytics
• Rendering and visualisation
• Other high-performance computing workloads

UKDedicated offers dedicated GPU capacity that can be configured to match your application's performance requirements.

GPU hosting gives you access to GPU-equipped infrastructure hosted in a data centre rather than keeping specialist hardware on your own premises. UKDedicated can provide dedicated GPU resources alongside the processor, RAM, SSD storage and networking needed for your workloads.

This can help you:

• Avoid a large upfront hardware cost
• Access dedicated rather than shared GPU capacity
• Choose infrastructure around your workload
• Scale capacity as requirements change

For organisations that need greater control over performance and infrastructure location, UKDedicated can build a GPU environment around those requirements.

Dedicated GPU servers are particularly effective for highly parallel workloads, while traditional CPU servers are designed to handle a broader range of general computing tasks.

The main differences include:

• GPUs can process many calculations simultaneously
• A CPU processor is suited to general-purpose computing
• GPU acceleration can significantly improve certain AI and machine learning workloads
• GPU infrastructure can also support rendering, modelling and analytics
• The right balance of GPU, cores and memory depends on the application

UKDedicated can help size the combination of GPU and compute resources around the workload you need to run.

GPUs are widely used for AI and machine learning because they can process many mathematical calculations simultaneously. This makes GPU servers well suited to training models, running inference and other highly parallel workloads.

They can be used for:

• Training large AI models
• Fine-tuning machine learning models
• Running production inference
• Processing large datasets
• Accelerating generative AI applications

UKDedicated provides dedicated GPU capacity for organisations that need consistent compute performance for demanding AI workloads.

Yes. A dedicated GPU server can give you access to GPU compute without requiring you to purchase and maintain the underlying specialist hardware yourself.

This approach can help you:

• Avoid a significant upfront cost
• Reduce responsibility for maintaining physical infrastructure
• Choose the GPU capacity appropriate for your workload
• Scale as requirements increase
• Access infrastructure support when required

UKDedicated GPU as a Service is designed for organisations that want dedicated capacity without continually investing in their own GPU hardware.

The right dedicated GPU server depends on your model, dataset, application and whether you are training, fine-tuning or running inference. Your wider configuration, including processor, cores, RAM, SSD storage and networking, also needs to match the workload.

UKDedicated supports NVIDIA GPU options suited to different requirements, including:

• NVIDIA L4 for inference, development and testing
• NVIDIA L40S for generative AI, inference and rendering
• NVIDIA H100 NVL for large models and demanding compute
• NVIDIA RTX PRO 6000 for AI, development and visualisation
• NVIDIA B200 and B300 for advanced AI training and highly demanding workloads

Rather than choosing an off-the-shelf configuration, UKDedicated infrastructure specialists can help identify the resources appropriate for your workload.

Yes. GPU dedicated servers can form part of a private AI environment where greater control over infrastructure and sensitive data is important.

A dedicated environment can help you:

• Work with proprietary or sensitive datasets
• Keep greater control over where workloads reside
• Run private AI applications and models
• Configure infrastructure around specific requirements
• Integrate GPU resources with your wider infrastructure

UKDedicated supports private AI environments on team.blue’s Europe-based infrastructure, helping organisations retain greater control over their applications, models and data.

GPU dedicated servers can be useful for sensitive workloads because dedicated infrastructure gives you greater control over where your applications, models and data reside.

When planning the environment, consider:

• The sensitivity of the data being processed
• Where the infrastructure and data are hosted
• Whether dedicated capacity is required
• How the environment integrates with existing systems
• The level of infrastructure management your team needs

UKDedicated hosts its GPU infrastructure in European data centres and can build environments around organisations that need greater control over data residency and infrastructure location.

UKDedicated dedicated GPU servers are hosted within European data centres, giving organisations greater control over infrastructure location and where their workloads and data reside.

This can be particularly relevant when you:

• Work with sensitive datasets
• Run private AI workloads
• Have specific data residency requirements
• Want infrastructure located in Europe
• Need direct access to European infrastructure expertise

For organisations that want to keep GPU workloads closer to home, UKDedicated combines European infrastructure with direct access to experienced infrastructure specialists.

Yes. GPU servers can be designed to scale from individual GPU resources through to larger environments as your requirements grow.

Your environment can be adapted around:

• Additional GPU capacity
• More compute cores
• Increased RAM and memory
• High-performance SSD storage
• Networking and bandwidth requirements

UKDedicated builds GPU infrastructure around the workload rather than limiting you to a standard configuration, making it possible to expand resources as requirements change.

Yes. UKDedicated GPU dedicated servers provide dedicated GPU capacity to help deliver consistent and predictable performance for demanding workloads.

Dedicated resources can be useful where you need:

• Predictable GPU performance
• Infrastructure built around a specific application
• A suitable processor and number of cores
• Configurable RAM and SSD storage
• Networking and bandwidth appropriate for data-intensive applications

UKDedicated can design and deploy the environment around your workload rather than requiring you to fit it around standard public cloud instances.

GPU cloud pricing commonly depends on consumption and the instance being used, while a dedicated GPU server can provide more predictable infrastructure costs. The right model depends on your workload, usage pattern and performance requirements.

When comparing options, consider:

• How frequently the GPU resources will be used
• Whether GPU cloud instances provide the required performance
• The cost of ongoing consumption
• Whether predictable infrastructure costs are important
• The compute, storage and bandwidth your workload requires

UKDedicated GPU as a Service provides dedicated capacity with predictable infrastructure costs, offering another option when standard public cloud GPU instances are not the right fit.

Optimising GPU cloud costs starts with matching infrastructure to the workload rather than paying for resources you do not need. Requirements can vary considerably between development, inference and large model training.

A good sizing process should consider:

• The GPU model and capacity required
• The number of compute cores
• Required RAM
• SSD storage performance and capacity
• Networking and bandwidth
• Whether workloads require dedicated capacity or flexible GPU cloud instances

UKDedicated infrastructure specialists can size the GPU, compute, storage and networking around your actual requirements, helping you avoid relying on an unsuitable standard configuration.

The choice between GPU cloud and a dedicated GPU server depends on how you use the infrastructure and the balance you need between flexibility, performance, control and predictable costs.

A dedicated environment may be worth considering when:

• Consistent performance is important
• You regularly run demanding production workloads
• Sensitive data requires greater infrastructure control
• Standardised public cloud instances do not suit your application
• Predictable infrastructure costs are a priority

UKDedicated GPU as a Service provides dedicated GPU capacity as an alternative when consumption-based public cloud infrastructure does not provide the balance your workload needs.

GPU dedicated servers can be configured around the operating system and software stack required by the workload. The wider platform can also be designed around the application rather than being restricted to an off-the-shelf configuration.

When planning your environment, consider:

• Your required operating system
• The application and software stack
• GPU and processor requirements
• SSD storage requirements
• Whether the infrastructure needs to be managed

UKDedicated can work with you to design, deploy and support a GPU environment around your specific technology requirements