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

$ 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

$ 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

$ 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.