Accelerate your ai roadmap
Complete, production-ready AI environment to move from model selection to deployment in minutes, not months
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 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
$ 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
$ 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
$ 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.
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.
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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.
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.
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