./ai-infrastructure

GPUs for training, fine-tuning and inference.

Dedicated GPU servers, clusters, storage and operations. Your AI, your data, your control.

Plan an AI deploymentCompare compute options

The service

Vonahost provides the compute, storage, network and operations that AI workloads need, from a single GPU server for inference to multi-node clusters for training.

We deliver the infrastructure, keep it running and scale it as usage grows.

01GPU compute

GPU compute for production AI.

Dedicated GPU servers and clusters for training, fine-tuning, inference. Sized and run by Vonahost.

  • Single-tenantHardware reserved for you
  • PrivateYour data and models stay in your environment
  • OperatedDrivers, stack and monitoring included
Plan a GPU deployment

Dedicated GPU servers for training, fine-tuning and inference.

02Purpose-built

Built for AI workloads.

Accelerator, memory, CPU, storage, interconnect: per job, not per catalog.

A fine-tuning run, a production inference service and a research cluster have different needs. We size each one separately, then configure the stack your team already uses: drivers, CUDA, PyTorch, vLLM, Triton or your own containers.

A ready-to-run environment, documented and monitored from day one.

  • GPUCurrent-generation data center accelerators, model confirmed per project
  • StorageLocal NVMe for datasets and checkpoints
  • NetworkPrivate, high-bandwidth links between nodes
  • IsolationDedicated hardware per customer

Storage for AI datasets

HBM3PCIe 5.0FP8

What you can run

Four enterprise uses for AI infrastructure.

01

Model training

Train from scratch or resume pre-training on your data. Multi-GPU nodes.

02

Fine-tuning

Adapt open models to your domain. Versioned datasets and checkpoints.

03

Inference and serving

Private APIs for language, vision, recommender models. Stable latency.

04

Private AI assistants

Search and Q&A on contracts, manuals and internal docs. No data exposed.

Server hardware with cooling fans illuminated in blue
ComputeDense GPU deployments need power, cooling and network planned together.

03What you get

01

Accelerators that fit the model

Data center GPUs selected for memory, throughput and budget. Model, quantity and lead time are confirmed in writing before you commit.

Request a GPU proposal
02

Close to your users

Deploy by latency and data residency: our sites or managed cloud regions.

Explore locations
03

Operated by engineers

Monitoring, driver and framework updates, capacity and deployment, by the team that built it.

Talk to the team
04

Private by design

Prompts, datasets, embeddings and weights never leave your environment. Access is logged and restricted to the people you authorize.

Security

Software

Standard tools, ready to use.

Environments are delivered with the frameworks your team already knows.

Frameworks
PyTorch, TensorFlow, JAX and Hugging Face libraries.
Serving
vLLM, Triton Inference Server, TGI or your own containers.
Orchestration
Docker and Kubernetes, with GPU scheduling.
MLOps
Experiment tracking, model registry, versioned data, monitoring.
Access
SSH, Jupyter, private API endpoints and VPN.
Models
Open-weight or your own models, on your infrastructure.

SECSecurity

Protected at every layer.

Protection starts at the network edge and continues through access, encryption and recovery.

Network protection

DDoS mitigation

  • Arbor mitigation in front of dedicated servers
  • Volumetric attacks filtered before they reach the server
  • DDoS-protected DNS

Network isolation

  • Private networking between environments
  • VPN access for administrators
  • Hardened operating system and firewall

Edge protection

  • Web application firewall
  • IP and country blocking
  • CDN and edge cache

Access and data

Access control

  • Role-based access
  • Multi-factor authentication
  • Audit logs

Encryption

  • TLS for data in transit
  • Encryption at rest for datasets and model artifacts
  • SSL certificates included on managed cloud

Recovery

  • Daily backups
  • Off-site copies
  • Restores tested on a schedule

Each proposal confirms protections per service.

FILTER

04Control

Full control of your infrastructure. And of your costs.

Dedicated GPU servers: defined locations, flat monthly USD pricing, single tenant.

  • PricingMonthly price per server, quoted in writing
  • LocationCountry stated in the proposal and the contract
  • TenancyNo neighbors on your hardware
  • ExitYour models, data and configuration remain yours
Request a GPU proposal
DEDICATEDUSD / MONTHNO NEIGHBORS

Private AI

Your data stays yours.

Prompts, documents, embeddings and model weights remain in your dedicated environment. Nothing is used to train third-party models, and access is logged and restricted to the people you authorize.

  • Data residencyChoose where data and models are hosted.
  • Access controlRole-based access, multi-factor authentication and audit logs.
  • EncryptionEncryption in transit and at rest for datasets and model artifacts.
PRIVATEAES-256AUDIT LOG

Use cases

Where AI pays off first.

  • Document intelligence

    Search, summarize contracts, policies, reports in seconds.

    01

  • Robotics and inspection

    Vision models that guide robots and catch line defects.

    02

  • Research and analytics

    Compute for simulations, forecasting and scientific models.

    03

How it works

From first workload to production.

01

Assess

We review models, data, expected traffic and compliance needs.

02

Size

GPU, memory, storage and network per workload, with price and lead time.

03

Deploy

Hardware provisioned, stack installed, your team gets access and docs.

04

Operate

We monitor, update and scale as usage grows.

Questions

Common questions.

Which GPUs do you offer?

We work with current-generation data center GPUs and select the model for each workload.

Can we bring our own models?

Yes. You can deploy your own models or open-weight models. Weights and data remain in your environment.

Is the hardware shared with other customers?

Dedicated GPU servers are reserved for a single customer. Where shared capacity is used, environments are isolated and this is stated in the proposal.

How is pricing defined?

Pricing depends on GPU type, quantity, term and support level.

Premium services

Servers for critical workloads.

Dedicated servers, VPS, data analytics. Sized per workload, run by Vonahost.

01

Dedicated servers

GPU and CPU servers reserved for your models.

  • Single-tenant hardware
  • Full root access
  • DDoS mitigation
Configure 02

VPS

VPS for APIs, queues and monitoring around the models.

  • KVM on NVMe
  • Reserved vCPU and RAM
  • Ready in minutes
Configure 03

Data analytics

Pipelines to prep training data and score models.

  • Pipelines
  • Warehouses
  • Dashboards
Configure

Workloads we run

AI inferenceModels served behind private endpoints.Data processingDatasets cleaned and prepared for training.APIs and microservicesAPIs that put models inside your products.

Next step

Plan your AI infrastructure.

Share the models, data volumes and latency targets. We return a sizing, a timeline and a proposal.

Plan an AI deploymentDedicated servers