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Private GPU servers & AI infrastructure
NVIDIA A100, H100 and B200 infrastructure, sourced around your business. Put circular enterprise technology to work with hardware supply, deployment and management from Seneca.
Private capacity. Practical control.
Commercial information, engineering knowledge and customer records deserve clear boundaries. Run selected AI workloads on infrastructure you control, with access and data handling built around your organisation.
Local models reduce dependence on external AI services for those workloads. You decide which applications stay private and where a hosted service adds value.
Busy teams need responsive tools, while inspection systems and data pipelines need dependable processing capacity. Dedicated GPUs give demanding applications a place to run close to their users and data.
We match compute, memory and connectivity to the workload, so the investment serves your operation rather than simply delivering a larger specification.
Growing AI use can turn a promising pilot into an unpredictable recurring bill. Owning capacity gives you another way to plan investment and allocate resources across teams.
We compare acquisition, electricity, hosting, software and support with the cost of external services. Circular sourcing brings existing enterprise hardware back into productive use.
Understand the processors
A GPU is a processor designed to perform many calculations at once. That makes it useful for the mathematics behind AI, image analysis and scientific computing. It works alongside the server’s CPU, memory, storage and network.
Established enterprise acceleration
A100 supports AI inference, model training and data analysis. Inference means using a trained model to produce an answer, extract information or recognise something in an image. Its Tensor Cores accelerate the matrix calculations at the heart of these tasks.
Available variants include 40GB and 80GB of GPU memory, in PCIe and SXM formats. A100 is worth considering for private assistants, document processing and research where the workload fits its memory and throughput.
Buying focus: memory size, server compatibility and the cost of running your actual workload.
More demanding AI workloads
H100 adds a Transformer Engine with FP8 support, designed to accelerate transformer models used in generative AI. It is a candidate for busier inference services, fine-tuning and training workloads that need more processing throughput.
Common H100 PCIe and SXM configurations have 80GB per GPU; H100 NVL has 94GB per GPU. SXM and PCIe systems differ in power, bandwidth and interconnection, so the exact variant matters.
Buying focus: simultaneous users, model size and the complete server’s ability to sustain performance.
Larger models and greater concurrency
B200 brings Blackwell architecture to intensive generative AI workloads. Greater memory capacity and newer Tensor Cores support larger models and substantial inference or training workloads in appropriately configured systems.
As a reference, DGX B200 combines eight GPUs with 1,440GB of total GPU memory—180GB per GPU. B200 infrastructure is a system-level purchase: the GPU fabric, cooling, power and software all matter.
Buying focus: sustained workload demand and the business case for a higher-capacity platform.
These are processor-family examples, not a stock list. Seneca confirms the exact hardware, memory, configuration, condition and warranty in your proposal.
What the specifications mean
GPU memory holds the model and its working data. Model size, numerical precision, conversation length and simultaneous requests all affect the capacity needed. More memory creates room for larger workloads; it does not by itself guarantee faster answers.
Bandwidth describes how quickly the processor can read and write its memory. It matters when generating AI responses, where repeatedly moving model data can limit throughput. We look at useful output and response time alongside headline computing power.
PCIe accelerators fit compatible server expansion slots. SXM modules require purpose-built platforms. NVLink connects supported GPUs at high speed for workloads spanning several devices; it does not make every application automatically use their combined memory.
FP16, FP8 and other formats describe how numbers are represented. Supported lower-precision methods can reduce memory use and increase throughput. The right choice preserves the quality your application needs and keeps the equipment productively occupied.
Practical applications
These illustrative use cases show where dedicated GPU infrastructure can support your operation.
An engineering team loses time searching manuals, service records and project files. A private assistant retrieves relevant material and prepares an answer with links back to the source, using permissions aligned with the team’s access.
Seneca brings the model, document search and GPU environment together. Staff get a quicker route to useful information, while the organisation controls where the system runs and which documents it can use.
The gain: less time searching, reusable organisational knowledge and a clear boundary around sensitive information.
Invoices, delivery notes and technical reports arrive in different formats. GPU-assisted document models extract information, identify document types and prepare structured records for the people and applications that need them.
Connect the results to your business workflow, with exceptions sent to the right team for review. A shared processing environment can serve several departments instead of each building a separate collection of tools.
The gain: less rekeying, shorter processing queues and more consistent records.
A production camera captures an item as it passes an inspection point. A locally deployed vision model analyses the image and raises a quality exception for the operator, with evidence attached to the job.
The same infrastructure can support image analysis and perception workloads used by warehouse robotics. Seneca connects the compute to the relevant applications and networks, keeping safety-critical machine controls within their engineered control systems.
The gain: earlier visibility of defects, less manual image review and operational data processed close to where it is created.
A specialist team needs an AI model to recognise its terminology or produce a consistent output format. Fine-tuning adjusts a suitable existing model using selected examples; evaluation checks the result against the tasks the team actually performs.
Dedicated GPU capacity gives developers a controlled place to experiment, compare versions and run the resulting service. Training larger models requires a different scale of investment, which we scope around the intended workload.
The gain: more relevant AI behaviour and a repeatable path from development to a working service.
Researchers and analysts can wait for shared resources while datasets continue to grow. GPU-enabled software accelerates supported simulations, numerical calculations and data-processing tasks, giving the team dedicated capacity for demanding work.
Seneca sources a platform around the software and data involved. We include storage throughput, memory and scheduling in the design so that the processors are fed with useful work.
The gain: greater control over research schedules and the capacity allocated to priority projects.
Circular GPU infrastructure
Enterprise equipment can still have substantial productive value when its original owner changes platform or refreshes an estate. Circular utilisation keeps that hardware working through sourcing and redeployment, giving another organisation access to its capabilities.
Seneca sources and resells GPU equipment against your requirements. We assess the fit between available systems, your workload and your budget, then agree the configuration, condition and support arrangements in a specific proposal.
Your quotation identifies the GPU variant and quantity, server specification, equipment condition, available test information, warranty provider and term, delivery expectations and included deployment services.
Support and software entitlements are checked for that offer. Refurbishment, certification and extended warranties are included only where specified.
Choose your deployment
Bring us your requirement and we source the GPU equipment or complete server. Your own team takes responsibility for installation and operation, with the agreed hardware warranty and support clearly set out.
A practical route for organisations that already have the facilities and technical capability to run their platform.
Seneca supplies and deploys the infrastructure in your environment. We assess power, cooling, rack space and connectivity, then configure the system and agree a handover around the workload it needs to run.
You gain private capacity with a coordinated installation and a clear operational starting point.
Combine GPU infrastructure with model deployment, applications, monitoring and ongoing support. Seneca manages the agreed environment and responsibilities, giving your team one place to turn.
Where hosting is required, we scope the location, facilities, access and service terms with you as part of the deployment.
Planning & costs
You do not need a finished specification to start. Tell us what you want to run or the problem you want to solve. We’ll assess your estate and help compare equipment, deployment and operating costs.
For steady workloads, owned capacity can offer a useful alternative to recurring cloud charges. For occasional bursts, external capacity may still make sense. We can plan a focused first deployment or move straight into a wider programme.
A private assistant, a growing inference service or a research platform—we’ll help turn the requirement into a practical proposal.
Talk to our teamYour questions
We source against the requirement, including GPU hardware and complete systems where available. The proposal makes clear what is included and checks compatibility with the intended deployment.
Circular describes keeping equipment in productive use through reuse and redeployment. Refurbishment is a specific condition or process, which we identify where it applies. Each offer sets out the actual equipment condition and warranty.
The model must be available for local deployment under suitable licence terms and fit the system’s memory and software capabilities. Hosted-only models remain external services. We match the platform to the models and applications you want to use.
A locally hosted model does not incur an external provider’s per-token API charge for its local processing. You still budget for hardware, electricity, software and operation. Any hosted APIs you connect retain their own charges.
We start with the model or application, workload volume and response times. A100, H100 and B200 serve different configurations and budgets; the best choice is the complete system that meets your needs at a sensible operating cost.
Pricing and availability depend on the exact equipment, condition, warranty and deployment scope. We confirm these in your proposal rather than publish a generic price for unlike configurations.
Bring the complete environment together
Your Private Infrastructure Partners
Source the right GPU platform and choose how Seneca helps you deploy and manage it.
Discuss your GPU requirements