AI Servers
Infrastructure built specifically for AI workloads — model training, fine-tuning, and inference — running on the same dedicated GPU capacity as our GPU Servers, with tooling and defaults tuned for AI teams rather than general-purpose compute.
What's included
Built to actually work the way you need it to
Infrastructure built specifically for AI workloads — model training, fine-tuning, and inference — running on the same dedicated GPU capacity as our GPU Servers, with tooling and defaults tuned for AI teams rather than general-purpose compute.
- Purpose-built for AI model training and inference
- Dedicated GPUs — never shared with other tenants
- PyTorch, TensorFlow, and CUDA ready out of the box
Performance you can count on
Modern hardware throughout
Current-generation CPUs and NVMe storage across every region, not last decade's hand-me-down hardware.
Built for demanding workloads
From analytics pipelines to media delivery, the infrastructure is sized for real production load, not just benchmarks.
Consistent performance at scale
Resources are provisioned so your performance doesn't degrade as usage grows — no noisy-neighbor slowdowns.
How it works
Pick your AI workload profile
Single-GPU for experimentation and fine-tuning, or multi-GPU for training larger models — sized to what you're actually running.
Launch with AI tooling pre-configured
PyTorch, TensorFlow, and CUDA are ready the moment your instance boots — no environment setup before you can start training.
Move from training to inference
Use the same platform to serve your trained model in production, scaling compute to match real request volume.
Included with every Hosted.one plan
Choose your plan
Full pricing details ->We love the simplicity and performance. Deploying servers is just a click away.
Frequently asked questions
Is this different hardware from GPU Servers?
No — it's the same dedicated GPU infrastructure as our GPU Servers product, positioned and configured specifically for AI training and inference workflows rather than general-purpose GPU compute.
Can I fine-tune an existing model, not just train from scratch?
Yes — fine-tuning and training from scratch both run on the same infrastructure. Size your instance to your dataset and model, not the other way around.
Can I serve inference traffic from the same account?
Yes — train and serve inference from the same platform, scaling compute up for training runs and back down for steady-state inference traffic.
Do I need to manage CUDA driver versions myself?
No — CUDA, PyTorch, and TensorFlow come pre-installed and version-matched, so you're not debugging driver compatibility before you can start work.
Ready to get started with AI Servers?
Create your account and deploy in under 60 seconds.