Table of Contents
Why GPU Server Hosting in 2026
Running AI workloads requires GPU compute that most organisations cannot justify purchasing and maintaining in-house. GPU server hosting provides access to NVIDIA hardware with professional networking, cooling, and power infrastructure without the capital expenditure or operational burden of running your own data centre.
In April 2026, GPU hosting has matured into a well-defined market with clear provider categories, standardised pricing, and reliable service levels. This buyer’s guide covers everything you need to evaluate providers and make the right selection for your AI workload.
Types of GPU Hosting
| Type | Pricing | Resources | Best For |
|---|---|---|---|
| Dedicated bare metal | Monthly flat rate | Guaranteed, exclusive | Production AI, consistent workloads |
| Cloud GPU instances | Per-hour | On-demand, shared infra | Burst workloads, experimentation |
| Spot / preemptible | Per-hour (discounted) | Can be interrupted | Batch processing, training |
| GPU marketplace | Varies | Variable quality | Budget experimentation |
For production AI inference that runs continuously, dedicated bare metal provides the most predictable cost and performance. Cloud instances suit variable workloads where you pay only for what you use. See the GPU hosting price comparison for current market rates across providers.
What to Evaluate in a Provider
Beyond price, several factors determine whether a GPU hosting provider meets your needs. Hardware age and quality vary significantly. Network bandwidth affects API serving performance. Data centre location matters for latency and data residency compliance. Support responsiveness determines how quickly issues are resolved.
Key evaluation criteria for AI workloads:
| Factor | What to Look For | Why It Matters |
|---|---|---|
| GPU hardware | Specific model, VRAM, generation | Determines model compatibility |
| Storage type | NVMe vs SATA SSD | Model loading speed |
| Network | Bandwidth, DDoS protection | API response delivery |
| Data location | Country of data centre | GDPR/compliance |
| Root access | Full OS control | Install any software |
| Support | Response time, AI expertise | Issue resolution speed |
GigaGPU’s private AI hosting with UK data centres, NVMe storage, and full root access addresses each of these criteria. For data residency requirements, see the UK AI regulation update.
Understanding Pricing Models
Hourly pricing looks cheaper but costs more for always-on workloads. A $0.69/hour RTX 5090 costs $500/month at 100% utilisation. A dedicated server at $250/month saves $250 monthly for the same hardware running the same hours. The break-even sits at approximately 50% utilisation.
Hidden costs to account for include storage fees, network egress charges, and persistent volume costs on cloud platforms. Dedicated servers from GigaGPU include storage and bandwidth in the monthly rate. Model your total costs using the LLM cost calculator.
Hardware Selection Guide
Match your hardware to your workload. The AI hardware buying guide covers detailed specifications. In summary:
| Workload | Minimum GPU | Recommended |
|---|---|---|
| Small models (7-13B) | RTX 3090 | RTX 5090 |
| Medium models (13-30B) | RTX 5090 | RTX 6000 Pro |
| Large models (70B) | 2x RTX 5090 | RTX 6000 Pro 96 GB |
| Image generation | RTX 3090 | RTX 5090 |
| Multi-model serving | RTX 6000 Pro | 2x RTX 5090 |
Use the tokens per second benchmark to verify throughput for your specific model-GPU combination before committing.
Find Your Perfect GPU Server
Dedicated GPU servers with transparent monthly pricing, NVMe storage, and full root access. Deployed in hours, not days.
Browse GPU ServersGetting Started
Start with the best GPU for LLM inference guide to identify the right hardware. Use the GPU vs API cost comparison to confirm self-hosting makes economic sense for your volume. Then choose between vLLM for production serving or Ollama for development setups. Browse the cost analysis section for detailed pricing breakdowns across different configurations.
