AI Services

Scalable AI Services.
100% sovereign. Zero egress fees. Deployed in minutes, not months.

Accelerate your ai roadmap

Scalable AI Services

Complete, production-ready AI environment to move from model selection to deployment in minutes, not months

LLM Inference API
Run open models through an OpenAI-compatible API. Point your existing code at us, change the base URL and key, nothing else.

what you get

  • OpenAI-compatible chat, completions and embeddings, no SDK rewrite
  • Curated open weights, Llama 3.3 70B, gpt-oss-120B, Qwen3, DeepSeek

USE CASES

  • Copilot or chatbot without owning GPUs
  • RAG over data already in your IC bucket, zero egress

import boto3

from openai import OpenAI

s3  = boto3.client("s3",

endpoint_url="https://eu-central-2.storage.impossiblecloud.com")

llm = OpenAI(base_url="https://api.impossiblecloud.com/v1", api_key=KEY)


doc = s3.get_object(Bucket="legal-eu", Key="msa-2026.txt")["Body"].read().decode()

answer = llm.chat.completions.create(

    model="llama-3.3-70b-instruct",

    messages=[{"role": "user", "content": f"Flag unusual indemnity terms:\n{doc}"}],

)

# Storage and inference in the same EU region — zero egress, one bill

On-Demand Instances
GPU compute the moment you need it. From a single dev box to a full Linux VM to your own private model endpoint. Per-minute billing, zero egress to your S3 storage, EU-sovereign by default.

what you get

  • Dedicated H100, H200, B200 and B300 per-minute billing, no commitment
  • SSH + JupyterLab ready to go, with CUDA, PyTorch, vLLM and Ollama preinstalled
  • Full-size Linux VMs with optional GPU passthrough (Terraform provider coming soon)
  • Private OpenAI-compatible endpoints on single-tenant GPUs at a flat hourly price
  • Mount your S3 storage directly — zero egress, ever

USE CASES

  • Experiments and benchmarks before reserving bare metal
  • A GPU dev machine for a day instead of buying hardware
  • Regulated workloads that must not share hardware
  • Steady inference traffic where per-token pricing gets expensive

$ ic gpu launch h200 --mount s3://training-data:/data

✓ Dedicated H200 in eu-central-2 — single-tenant, per-minute billing

✓ /data → your IC bucket, zero egress

$ ic gpu exec dev-box "python bench.py --input /data/eval.parquet"

[bench] throughput: 1.9k img/s

[bench] results written to /data/results/

$ ic gpu pause dev-box

✓ Paused after 38 min — billing stopped, storage persists

Fine-tuning
Fine-tune open models on your own data. Upload a dataset, pick a base model, get a private endpoint.

what you get

  • LoRA or full fine-tuning on any catalogue model
  • Training data read straight from your IC bucket, stays in the EU
  • One click to deploy as a private Model Deployment

USE CASES

  • Legal-tech model that knows your document style
  • Support model trained on resolved tickets

$ ic finetune start \

    --base llama-3.3-70b-instruct \

    --adapter lora --rank 16 \

    --data s3://support-eu/tickets-2025.jsonl

280k examples streamed from your IC bucket (eu-central-2, zero egress)

→ step 1200/3600 · train_loss 0.92 · eta 41m

→ step 3600/3600 · train_loss 0.58 · done

$ ic deploy tickets-v1 --private

✓ Single-tenant endpoint live — OpenAI-compatible

Managed Kubernetes
A dedicated isolated Kubernetes cluster. GPU quotas, full kubeconfig, your own Helm charts. We run the control plane.

what you get

  • One isolated cluster per tenant, no shared control plane
  • GPU and CPU nodes from our own fleet
  • Upgrades and patching handled for you

USE CASES

  • Your AI app stack on one sovereign cluster
  • Burst GPU jobs next to your stored data

$ ic k8s kubeconfig prod-cluster > ~/.kube/config

$ kubectl get nodes

NAME         STATUS   GPU

gpu-node-1   Ready    8× H100

gpu-node-2   Ready    8× H100

cpu-node-1   Ready    —


$ helm install ai-stack ./charts/app

✓ Deployed on your isolated cluster — no shared control plane

HPC Clusters
Tightly coupled GPU clusters for large-scale training with high-bandwidth interconnect, parallel storage access, built for multi-node jobs.

what you get

  • Multi-node GPU clusters with high-speed fabric
  • Tuned for distributed training and tightly coupled workloads
  • Direct access to your S3 storage, zero egress

USE CASES

  • Multi-node training runs that outgrow a single box
  • Protein folding and scientific simulation
  • Large-scale distributed workloads with heavy node-to-node traffic

$ torchrun --nnodes 8 --nproc_per_node 8 \

    --rdzv_endpoint head-node:29500 train.py

[NCCL] 64 GPUs linked over high-bandwidth fabric

[rank0] epoch 1 | step 500 | 380k tokens/s

✓ Checkpoints written to s3://checkpoints-eu — zero egress

Managed Slurm
A fully operated Slurm scheduler on top of dedicated GPUs. You submit jobs; we run the scheduler and the queue.

what you get

  • We operate the scheduler. You just submit jobs
  • Real queueing and prioritization for large batch runs
  • No cluster admin, no scheduler maintenance

USE CASES

  • Research lab queues hundreds of jobs overnight
  • Batch inference over millions of documents
  • Teams that want Slurm without hiring an HPC admin

$ sbatch --nodes=4 --gres=gpu:8 train.slurm

Submitted batch job 4217

$ squeue --me

JOBID  PARTITION  NAME   ST  NODES

4217   gpu        train  R   4


# We run the scheduler and the queue. You just submit jobs.

Ready to scale your AI?

From model selection to production deployment in minutes, not months. Our fully managed AI services covering LLM inference, model deployments, managed Kubernetes, and HPC remove infrastructure complexity so your developers focus on building, not babysitting clusters. Whether you're launching serverless endpoints or orchestrating large training runs, you get one unified ecosystem with the agility to scale rapidly and the ironclad data privacy that only comes from compute and data co-located in sovereign European data centres.

The Full-Stack Infrastructure Built for Heavy AI Workloads

Accelerated AI requires more than just raw chips; it demands that data and compute live under the same roof. The Impossible Cloud AI Suite integrates managed AI services, containerized GPU workspaces, and high-throughput S3 object storage into a single identity, billing engine, and API surface. By eliminating the distance between your data and your models, we erase data gravity bottlenecks and cloud tax, giving you a seamless, single-vendor experience.

"The combination of co-located storage and GPU compute is what made the architecture work. Running batch inference against millions of pathology images at scale requires the data and the compute to be in the same place and it has to be in Europe."
CIO, Leading German AI-Powered Medical
Imaging Enterprise (Early Access Customer)

Need Raw GPU Power for Custom Workloads?

While our AI services provide fully managed environments, some enterprise workloads demand direct, unmanaged hardware control. If your models require dedicated bare-metal performance, maximum memory configurations, or a custom cluster layout, our team can configure and deploy infrastructure to your exact specifications.

GPU server 3D render