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Launch instances and manage your compute resources.
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Launch self-serve GPU compute for demanding work.
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Compute with Hivenet
RTX 4090 from €-/h. RTX 5090 from €-/h. Per-second billing. Network traffic included.
Self-serve GPU compute. Sovereign by architecture. Available in France, UAE, and USA.
Who runs on Compute with Hivenet
Researchers, startups, studios, and enterprise teams run production workloads on this infrastructure. Not a sandbox.
Who uses Compute.
Used by independent builders, researchers, students, creators, and teams testing ideas before they become larger deployments.
Jupyter notebooks, fast GPU access, green enzymes research.
Hivenet makes our work much easier. With Jupyter notebooks, fast access to GPUs, and reliable infrastructure across regions, we’ve been able to speed up our research on green enzymes for industrial use. It feels like a real step forward in compute platforms.
The possibility of easily obtaining instances with graphics cards for a really cheap price. I use it a few times a month and it's really perfect.
Sustainable, European, sovereignty, fair pricing.
What we like about Hivenet is that it matches how we think about AI: sustainable, efficient, and grounded in Europe. The distributed model gives us security, the option to choose European models strengthens our sovereignty message, and small details like pausing instances or fair pricing just make it practical for us to use day to day.
We've moved over from AWS and GCP to hive. It's a great way to save on costs.
GPU reliability for scaling AI work with students.
Hivenet’s GPUs have been key to scaling our AI work. They let us run advanced models smoothly, so our interactions with students stay fast and responsive. That reliability has made a real difference for us.
Super quick setup. No BS/straightforward pricing. Fast cold starts. Reliable machines.
What you can do with it.
Training and fine-tuning
Train on 4090 and 5090 instances. Reusable environments. Per-second billing. Your training data stays on your infrastructure.
Rendering and creative work
Blender, video encoding, upscaling. Dedicated GPU instances. Not queued. Not shared.
Research and experiments
Simulations and notebooks. On-demand. No minimum commitment.
Inference
Run local models at 737 tokens/s (RTX 4090) or 45.4ms TTFT (RTX 5090). Private. No third-party API calls.
Get started fast.
Swipe left to see more
Managed vLLM
Deploy an OpenAI-compatible inference endpoint without building the serving layer yourself.
Ready templates
Ubuntu, PyTorch, and Jupyter Notebook. Pre-configured. Start in under 60 seconds.
Custom templates
Save your setup. Reuse it.
Currently on RunPod or Google Colab?
Bring your setup. Same tools. European infrastructure. Lower cost.
Per-second billing. No egress fees. Storage included.
RTX 5090
1 × - 8 ×
vCPU - - -
RAM - - - GB
Disk space - - - GB
Bandwidth - Mb/s
RTX 4090
1 × - 8 ×
vCPU - - -
RAM - - - GB
Disk space - - - GB
Bandwidth - Mb/s
vCPU
2 × - 32 ×
RAM - - - GB
Disk space - - - GB
Bandwidth - - - Mb/s
Start now. No approval process.
No sales call required. Credit card is all you need.
Your GPU instances run on distributed infrastructure
No legal pathway for foreign government data requests.
77% greener than centralized cloud.
No offsets.
Need Compute for a team?
If you need regional deployment, private AI, procurement support, or a larger rollout, explore Compute for business.
