Built for Scale.
Engineered for Sovereignty.
A comprehensive AI ecosystem designed to accelerate your breakthroughs. From massive distributed training runs to low-latency inference-all running on 100% sovereign European infrastructure.
One platform. Every interface.
CLI, SDK, API, or Console. Manage GPU infrastructure and deploy workloads from wherever you work.
$ cubitics auth login ✓ Authenticated as team@starlex.ai $ cubitics cluster create \ --name foundation-run \ --gpus 64 --type gb200 \ --region eu-central ✓ Cluster "foundation-run" ready ID: cl-7f2a9b GPUs: 64× NVIDIA GB200 Network: NVLink + InfiniBand Region: eu-central 🇪🇺 $ cubitics deploy --model ./checkpoint \ --endpoint prod --autoscale ✓ Deployed → https://api.cubitics.com/v1/prod Latency: 12ms Scale: 1–16 replicas█
pip install cubiticsfrom cubitics import Client client = Client() # Provision a GPU cluster cluster = client.clusters.create( name="foundation-run", gpus=64, gpu_type="gb200", region="eu-central", ) # Launch distributed training job = cluster.train( script="train.py", framework="deepspeed", config="ds_config.json", ) # Stream logs in real-time for line in job.logs(): print(line)
pip install cubiticsPOST /v1/clusters HTTP/1.1 Host: api.cubitics.com Authorization: Bearer ck_live_... Content-Type: application/json { "name": "foundation-run", "gpus": 64, "gpu_type": "gb200", "region": "eu-central" } HTTP/1.1 201 Created { "id": "cl-7f2a9b", "status": "provisioning", "gpus": 64, "region": "eu-central", "created_at": "2026-02-22T09:14:00Z" }
https://api.cubitics.com/v1console.cubitics.comOur inference cluster shows high latency on batch requests. How can we optimize?
Based on the cluster config, I recommend:
1. Enable continuous batching
2. Implement model sharding across GPUs
3. Optimize KV-cache allocation█
console.cubitics.com/model-hubEverything you need to build and deploy AI.
From training your first model to running production inference at scale. All on sovereign European infrastructure.
AI Model Training
Train foundation models, fine-tune LLMs, or run distributed ML experiments. From a single GPU to multi-thousand GPU training runs with automatic checkpointing and recovery.
Model Hosting & Inference
Deploy trained models as production-ready API endpoints. Built-in auto-scaling and load balancing.
Data Storage
Sovereign object storage, high-performance NVMe block storage, and shared file systems.
On-Premise & Hybrid
Need GPU hardware at your location? We deliver fully managed infrastructure on-site.
Platform Management & Security
Cloud Console, CLI, REST API. Manage everything from a single control plane. EU-sovereign by design. GDPR-compliant, AI Act ready, no CLOUD Act exposure. Full encryption at rest and in transit.
Your stack. Our GPUs.
Standard tools, standard APIs, standard formats. No proprietary abstractions. Your existing ML stack works out of the box. Migrate in, migrate out.
Get early access to the 
platform.
First GPU capacity and platform access planned from Q2 2026. Your early commitment as a Founding Partner helps finance the build. With preferred pricing and guaranteed availability.