Serve your own models on a GPU you do not share, at a price you can plan.
A dedicated 96 GB GPU with the inference stack installed, monitored by people who have run it before.
The problem.
Why this workload is usually the first to leave the cloud.
Hourly GPU rental punishes steady workloads: a model that serves traffic all day costs more by the hour than a dedicated card, and the ops work of drivers, runtimes and monitoring still lands on your team.
How Infraexa runs it.
The configuration we deliver and the operations that come with it.
- 1
Accel 96 delivered with CUDA, the container toolkit and vLLM or Triton configured and load-tested
- 2
Your chosen open-weight model pulled and serving before handover
- 3
GPU utilisation, memory and token throughput on your dashboard, with Exa watching for thermal and memory drift
- 4
Fine-tuning runs scheduled around inference traffic, with checkpoints backed up nightly
What changes for you
- Predictable monthly cost for steady inference
- Private data stays on hardware that is yours alone, in the EU
- No driver, runtime or kernel surprises
Recommended packages.
Start with the first; the second is the usual companion or next step.
- Accel 96Accel
A 96 GB Blackwell GPU with 512 GB of system memory, ready for inference day one.
- CPU
- 24 cores / 48 threads
- Memory
- 512 GB DDR5 ECC
- GPU
- NVIDIA RTX PRO 6000 Blackwell, 96 GB
€6,780per month - Core 48Core
48 Zen 4 cores, 256 GB and fast NVMe for your main application tier.
- CPU
- 48 cores / 96 threads
- Memory
- 256 GB DDR5 ECC
- Storage
- 2 × 3.84 TB NVMe (mirrored)
€2,540per month
Running ai inference and fine-tuning? Describe it and get a sized quote.
A quote within one business day, from an engineer rather than a sales script. No setup fee, three-month minimum, delivery in 48 hours.