GPU infrastructure for AI models: hardware sizing, hosting, autoscaling and failover
We calculate the hardware your models and load really need, deploy it and set it up so the system handles peaks and survives the loss of a server.
What hurts
You do not know which GPU to buy or rent. The vendor recommends the most expensive one, and you are afraid to overpay or end up underpowered. A mistake here costs thousands of dollars and months waiting for a new delivery.
The model runs on one server. When it goes down, call analytics, video and agents all stop, and one person fixes it at night.
GPUs sit idle most of the day, but at peak hours the request queue grows and customers wait. You pay for capacity you do not use and still run short of it.
What we build
- Sizing the GPUs your models and load need
- Cost comparison of owned hardware, rental and cloud
- Deployment in Docker or Kubernetes with vLLM and vision models
- Autoscaling for peak hours
- A standby node and automatic failover
- Network protection, access control and encryption
- Monitoring of GPU load, temperature and queues
What you get
- Running GPU infrastructure with access for your team
- Infrastructure as code in your repository
- A cost calculation and a scaling plan
- Dashboards and alerts on load and failures
- A recovery runbook and a tested failure scenario
How we measure the result
- Average and peak GPU utilisation
- Model response time at peak hours
- Recovery time after losing one node
- Monthly infrastructure cost per unit of load
Systems and stack
Industries
Questions
How much does GPU infrastructure cost?
Our work starts at $10,000. Hardware or rental is billed separately and is often the bigger part of the budget. So we start with a calculation comparing a server purchase, a rented dedicated server and a mixed setup.
Should we buy servers or rent them?
It depends on load and data requirements. Steady round-the-clock load is often cheaper on owned hardware. Uneven or temporary load is better rented. Banks and clinics often ask for a dedicated server. We calculate on your numbers.
Which models can you host?
LLMs on vLLM, speech recognition models, computer vision models for video analytics and embedding models for search. We run this stack ourselves for call analysis and camera video.
How long does deployment take?
Usually 4–8 weeks. The timeline is often set not by our work but by hardware delivery or approval from your security team. Renting a dedicated server is usually faster than buying your own hardware.
Who maintains it after launch?
We can run the infrastructure under an SLA with round-the-clock monitoring and incident response. Or we hand it to your administrators with documentation and a tested recovery scenario.
Where will the servers physically be?
There are two options: your own server room or a dedicated server in the EU (Hetzner, Germany). Banks and clinics often pick a dedicated server with no resources shared with other customers. We record the location in documentation for your security team, DPO and auditor.
Related services
More in this section
Describe the task. We reply within one business day, the first call is with the founder.
Describe the task. The first call is with the founder.
We reply within one business day. 30 minutes to tell you whether it is worth doing and what it costs.