Dedicated GPU vs Cloud GPU: What Is the Difference?
Choosing between dedicated GPU hosting and cloud GPU instances is one of the most consequential infrastructure decisions for AI teams. The two models differ fundamentally in how resources are allocated, how you pay, and how much control you get. Understanding these differences is critical whether you are hosting open-source LLMs, running image generation pipelines, or building real-time AI applications.
Dedicated GPU hosting gives you an entire physical server with one or more GPUs exclusively reserved for your workloads. No other tenants share the hardware. You get full root access, bare-metal performance, and a fixed monthly cost regardless of utilisation.
Cloud GPU instances (from AWS, GCP, Azure, or serverless providers) offer virtualised GPU access on shared infrastructure. You typically pay per hour or per second, and your instance may share the physical GPU with other tenants through virtualisation or time-slicing.
Feature-by-Feature Comparison
| Feature | Dedicated GPU (GigaGPU) | Cloud GPU (AWS/GCP/Azure) | Serverless GPU (RunPod/Replicate) |
|---|---|---|---|
| Hardware Access | Bare-metal, exclusive | Virtualised, shared host | Containerised, shared |
| Billing | Fixed monthly | Per-hour (+ storage, network) | Per-second |
| Cost Predictability | 100% predictable | Variable | Highly variable |
| Cold Starts | None | Minutes (boot time) | Seconds to minutes |
| GPU Availability | Guaranteed (reserved) | Variable (capacity limits) | Variable (spot market) |
| Root Access | Full | Limited (VM-level) | Container-level only |
| Network Performance | Dedicated bandwidth | Shared, variable | Shared, variable |
| Data Privacy | Fully isolated | Hypervisor-separated | Shared infrastructure |
For a specific comparison of serverless versus dedicated models, see our detailed guide on serverless GPU vs dedicated GPU costs and trade-offs.
Cost Analysis: When Dedicated Wins
The cost comparison depends entirely on your utilisation pattern. Cloud GPUs charge by the hour, which is efficient for workloads that run a few hours per day. But for always-on or high-utilisation workloads, the hourly billing accumulates to far more than a dedicated server costs monthly.
| GPU | AWS/GCP (730 hrs/mo) | GigaGPU Dedicated | Breakeven Utilisation |
|---|---|---|---|
| RTX 6000 Pro 96 GB | ~$2,200-2,800/mo | From ~$799/mo | ~30% |
| RTX 6000 Pro 96 GB | ~$3,500-4,200/mo | From ~$1,599/mo | ~40% |
| RTX 5090 equiv. | Not available on big cloud | From ~$299/mo | N/A |
Major cloud providers also add charges for storage, data transfer, and static IPs that are typically included with dedicated hosting. Use the GPU vs API cost comparison tool to calculate your total cost of ownership. Our cost per million tokens analysis shows how these differences play out for LLM workloads specifically.
Get More GPU for Less Money
Dedicated GPU servers deliver bare-metal performance at a fraction of cloud GPU pricing. Fixed monthly cost, no hidden fees, guaranteed availability.
Browse GPU ServersPerformance Differences That Matter
Beyond cost, dedicated GPU hosting offers measurable performance advantages that matter for production AI:
- No noisy neighbours – Cloud GPU instances share the physical host with other VMs. Memory bandwidth and PCIe throughput can be affected by other tenants. Dedicated servers have no contention.
- Consistent latency – Virtualisation overhead adds 5-15% latency on cloud instances. Bare-metal servers deliver the GPU’s full rated performance consistently.
- Full VRAM access – Some cloud providers reserve a portion of GPU VRAM for the hypervisor. Dedicated servers give you the full 24/48/80 GB.
- NVLink and multi-GPU – Multi-GPU cluster configurations on dedicated hardware provide full NVLink bandwidth for model parallelism, which is often degraded on virtualised cloud infrastructure.
See the tokens per second benchmark for real-world inference performance across different GPU and model combinations.
Which Is Better for Your Use Case?
Here is a practical decision framework:
| Use Case | Best Choice | Why |
|---|---|---|
| Production LLM inference (24/7) | Dedicated GPU | Lowest cost, no cold starts, predictable billing |
| Short training runs (hours) | Cloud GPU | Pay only for what you use |
| AI chatbot / API service | Dedicated GPU | Always-on, consistent latency required |
| Occasional experimentation | Cloud GPU / Serverless | Low utilisation, burst access |
| Regulated industries (healthcare, finance) | Dedicated GPU | Full data isolation, compliance |
| Image/video generation service | Dedicated GPU | High GPU utilisation, latency-sensitive |
If your workload fits the dedicated model, our self-host LLM guide walks you through the full setup process.
The Hybrid Approach
Some teams run a hybrid strategy: dedicated GPU servers handle the baseline production load, while cloud burst capacity handles traffic spikes. This works well if your traffic is highly variable but has a consistent floor.
For example, you might run your primary vLLM inference server on a dedicated GigaGPU instance for predictable traffic, and route overflow to a serverless provider like RunPod during peak periods. This captures the cost savings of dedicated hosting for 80%+ of your traffic while maintaining elasticity.
Our Recommendation
For the vast majority of production AI workloads, dedicated GPU hosting is the better choice. It delivers lower costs at any utilisation above roughly 30-40%, eliminates the unpredictability of cloud spot markets, and provides the bare-metal performance that AI inference demands.
Cloud GPUs make sense for short-term training jobs and low-frequency experimentation. But if you are running private AI hosting for production applications, dedicated servers from GigaGPU give you the best combination of price, performance, and control. Browse the full range of options in our alternatives category, or jump straight to choosing the right GPU for your workload.
