What Shared vs Dedicated GPU Means
When cloud providers and GPU platforms advertise GPU instances, they’re often selling shared GPU resources. Multiple customers’ workloads share the same physical GPU through virtualisation (MIG, vGPU, or time-slicing), meaning you get a fraction of the GPU’s total capability. Dedicated GPU hosting gives you entire physical GPUs exclusively allocated to your workloads — no sharing, no virtualisation overhead, full bare-metal performance.
This distinction matters enormously for AI workloads. LLM inference, model training, and image generation are GPU-bound tasks where every percentage of compute matters. On shared infrastructure, you’re paying for a GPU but receiving only a portion of its capability. With dedicated GPUs, you get 100% of the hardware, 100% of the time.
Performance Impact of Shared GPUs
Shared GPU infrastructure impacts AI workloads in several measurable ways:
Throughput reduction: On a shared GPU, your maximum throughput is capped at whatever fraction you’ve been allocated. A shared RTX 6000 Pro giving you 40GB of the 80GB total means roughly half the batch size and half the throughput of a dedicated card.
Latency variability: Other tenants’ workloads create unpredictable latency spikes. Your P50 latency might look fine, but P99 latency on shared infrastructure can be 5-10x worse than on dedicated hardware. For production API endpoints, this kills user experience.
Memory contention: GPU memory is a fixed resource. On shared GPUs, you can’t load large models or maintain large batch sizes. On dedicated RTX 6000 Pro 96 GB GPUs, you can run 70B parameter models at full precision.
No noisy neighbours: On dedicated hardware, your performance is deterministic. Check our inference benchmarks for real throughput numbers on dedicated GPUs.
Cost Analysis: Shared vs Dedicated
| Infrastructure | Apparent Monthly Cost | Effective Performance | Cost per Actual Token/s |
|---|---|---|---|
| Shared GPU (cloud) | $300-800/mo | 30-60% of full GPU | High (per-token API or per-hour) |
| Shared GPU (marketplace) | $100-400/mo | Variable (20-80%) | Unpredictable |
| Serverless GPU | Usage-dependent | Full when active, zero when idle | High (includes cold start overhead) |
| Dedicated GPU (GigaGPU) | From ~$200/mo | 100% of full GPU | Lowest at production volumes |
The cost per million tokens on dedicated hardware dramatically outperforms shared infrastructure when measured on actual throughput rather than listed price. Use our cost comparison tool to see the real numbers for your workload.
Full Comparison Table
| Factor | Shared GPU | Dedicated GPU (GigaGPU) |
|---|---|---|
| GPU Access | Fraction of physical GPU | Entire physical GPU |
| Performance | Variable, capped | Full bare-metal |
| Latency Consistency | Variable (noisy neighbours) | Deterministic |
| GPU Memory | Partitioned (limited) | Full (80GB on RTX 6000 Pro) |
| Pricing | Per-hour or per-token | Fixed monthly |
| Cold Starts | Common | None |
| Data Privacy | Multi-tenant | Single-tenant |
| Model Size Limit | Constrained by partition | Full GPU memory |
| Root Access | Usually no | Full |
| UK Datacenter | Sometimes | Yes |
Use Cases: When Each Makes Sense
Shared GPUs make sense for:
- Quick prototyping and experimentation
- Very sporadic, low-volume workloads
- Student and hobbyist projects
- Workloads that don’t need consistent latency
Dedicated GPUs are essential for:
- Production inference APIs with latency SLAs
- Running large models (70B+ parameters)
- High-throughput batch processing
- Privacy-sensitive workloads
- Training and fine-tuning
- Any workload where consistent performance matters
For production AI — chatbots, image generation, LLM serving, speech synthesis — dedicated GPUs are not optional. They’re the minimum viable infrastructure for reliable service.
Hidden Costs of Shared GPUs
Shared GPU infrastructure carries costs that don’t appear on the invoice. Engineering time spent debugging performance variability. Failed SLAs due to noisy-neighbour effects. Reduced model quality from forced quantisation to fit smaller memory partitions. Customer churn from inconsistent response times.
The total cost of ownership calculation must include these hidden costs. When you do, dedicated hosting wins by an even wider margin. Teams that switch from shared cloud GPUs to dedicated hardware consistently report both cost savings and performance improvements.
For teams evaluating infrastructure options, our comparisons of Vast.ai, Paperspace, RunPod, and other providers highlight the shared vs dedicated distinction for each platform.
Why Dedicated GPUs Win for Production AI
The shared vs dedicated GPU decision is straightforward for production AI workloads. Dedicated GPUs deliver better performance, predictable costs, and complete resource isolation. Run vLLM or Ollama at full bare-metal speed with no compromises.
GigaGPU’s dedicated GPU servers give you enterprise hardware at fixed monthly pricing in a UK datacenter. No shared resources, no cold starts, no billing surprises. For larger workloads, multi-GPU clusters scale linearly. See our self-hosting guide to get started, choose your hardware with our GPU selection guide, or explore the full alternatives directory and infrastructure comparison for a complete view.
Switch to Dedicated GPU Hosting
Fixed pricing, bare-metal performance, UK datacenter. No shared resources, no cold starts.
Compare GPU Server Pricing
