NVIDIA RTX 4000 Ada Generation. 20 GB Pro GPU on Demand
Rent the RTX 4000 Ada.20 GB GDDR6, 6,144 CUDA cores, 4th-gen Tensor cores. Excellent price-performance for AI inference, small-to-mid model fine-tuning, and pro rendering. Deploy in minutes from German data centers.
Why the RTX 4000 Ada
Best Price-Performance
Entry-level pro GPU at a fraction of the RTX 6000 Ada cost, well suited for cost-sensitive workloads.
20 GB GDDR6 ECC
Plenty of VRAM for 7B–13B LLMs at full precision, Stable Diffusion XL, large rendering scenes.
AI Inference Ready
4th-gen Tensor cores with FP8, bfloat16 and sparsity. vLLM, ComfyUI, all the AI frameworks.
Pro Rendering
3rd-gen RT cores, full OptiX support, with solid Blender, Octane and V-Ray performance.
Multi-GPU Available
Configure 1x, 2x, 4x or more in a single server, scale linearly for distributed training.
Hosted in Germany
GDPR-compliant data centers, low EU latency, no US-cloud lock-in.
What's Included
- 1× NVIDIA RTX 4000 Ada Generation (20 GB GDDR6 ECC)
- Dedicated CPU cores and RAM
- Fast NVMe SSD storage
- 1× IPv4 + /64 IPv6
- Generous traffic, no overage
- Full root access, bring your own AI stack
- Multi-GPU configurations and snapshots available
RTX 4000 Ada Specs
| VRAM | 20 GB GDDR6 ECC |
| CUDA Cores | 6,144 |
| Tensor Cores (4th-gen) | 192 |
| RT Cores (3rd-gen) | 48 |
| FP32 Performance | 26.7 TFLOPS |
| FP8 (Tensor) | 306 TFLOPS |
| Memory Bandwidth | 360 GB/s |
| TDP | 130W |
RTX 4000 Ada plans from €99.99/month. Multi-month contracts unlock additional discounts.
→ All GPU ServersRent RTX 4000 Ada
RTX 4000 Ada plans from €99.99/month. Multi-month contracts unlock additional discounts.
Frequently Asked Questions
What's the RTX 4000 Ada best for?+
It's the sweet spot for cost-conscious workloads: serving 7B–13B LLMs, Stable Diffusion / SDXL inference, fine-tuning small models with QLoRA, computer vision, professional rendering, CAD and visualization.
How does it compare to the RTX 6000 Ada?+
Same Ada Lovelace architecture and feature set, but smaller silicon: 6,144 vs. 18,176 CUDA cores, 20 GB vs. 48 GB VRAM. Performance is roughly 1/3 of the 6000 Ada, but the price is also significantly lower, making it the better choice when 20 GB is enough.
Can I run a 13B-parameter LLM on it?+
Yes.13B-parameter LLMs in FP16 fit comfortably in 20 GB VRAM. With 4-bit quantization, even 30B-class models work.
Does it support FP8?+
Yes.4th-gen Tensor cores have full FP8 support. vLLM with FP8 KV cache, NVIDIA Transformer Engine, all standard optimizations work natively.
Can I scale to multiple GPUs?+
Yes. Multi-GPU configurations (2x, 4x, 8x) are available for distributed inference and training.
Is the GPU dedicated?+
Yes. No time-slicing, no sharing. You get the full 20 GB VRAM and full compute capacity.