Server comparison
GPU servers
NVIDIA GPU servers for inference, rendering, and AI startups. Compare VRAM and monthly cost instead of renting hyperscaler GPUs by the hour. Still on APIs? Read self-host LLM vs API. Unsure whether you need 24 GB or 80 GB? Read how much GPU VRAM you need. Running a 35B locally on used Pascal? See the 2× P100 build.
Quick selection guide
Choose the GPU tier before comparing providers
VRAM is often the first constraint for AI. Choose the smallest tier that comfortably fits your model, then compare the full server configuration and quote.
| GPU model | Best fit | Relative tier | VRAM | Buying guidance |
|---|---|---|---|---|
| NVIDIA L4 | Inference, video processing, lightweight AI applications | ●●●●● | 24 GB | Good starting point when efficient inference matters more than training capacity. |
| RTX PRO 4500 Blackwell | AI development, rendering, data analysis, visual workloads | ●●●●● | 32 GB | Balanced capacity for teams moving beyond entry-level inference. |
| RTX PRO 5000 Blackwell | Deep learning, larger AI models, advanced rendering | ●●●●● | 48 GB / 72 GB | Choose when model size, batch size, or render complexity needs more headroom. |
| RTX PRO 6000 Blackwell | Large AI models, HPC, scientific simulation, enterprise AI | ●●●●● | 96 GB | For workloads where VRAM limits and time-to-result outweigh budget. |
Tier is a practical buying guide, not a benchmark score. Confirm the final CPU, RAM, storage, network, location, and current price with the provider.