NVIDIA GPU — CUDA ready

Infrastructure built for AI workloads

NVIDIA A100 and H100 with CUDA ready, PyTorch and TensorFlow pre-installed and NVLink between GPUs — start training your models in minutes, not days. Your GPU, fully isolated.

Choose your GPU server Talk to our AI team

CUDA-ready on first boot

1,979 TFLOPS FP16 per H100

0 GB HBM VRAM per GPU

0 GB/s NVLink GPU interconnect

24/7 AI-savvy expert support

Choose the right GPU power for your workload

From lightweight inference to massive LLM training — dedicated GPUs, never shared, never time-sliced.

AI A100

Fine-tuning & inference

^$1,199.00/mo billed monthly — cancel anytime

NVIDIA A100
80 GB HBM VRAM
16 vCPU cores
120 GB RAM
1 TB NVMe Gen4

CUDA + cuDNN pre-installed
PyTorch & TensorFlow ready
Full GPU isolation

Deploy this GPU CUDA-ready in minutes

Best for training

AI H100

Serious model training

^$2,499.00/mo billed monthly — cancel anytime

NVIDIA H100 SXM
80 GB HBM VRAM
26 vCPU cores
200 GB RAM
2 TB NVMe Gen4
NVLink interconnect

Everything in AI A100
HBM3 — 3.35 TB/s bandwidth
NVLink-ready for scaling

Deploy this GPU CUDA-ready in minutes

AI Cluster

Multi-GPU NVLink fabric

Price on request Custom clusters, sized with our engineers

4× NVIDIA H100 SXM
320 GB HBM VRAM
96 vCPU cores
768 GB RAM
8 TB NVMe Gen4
NVLink interconnect

4× H100 over NVLink 600 GB/s
Distributed training tuned
Dedicated solutions engineer

Every AI Server includes

CUDA Toolkit + cuDNN pre-installed
PyTorch + TensorFlow ready
Full GPU isolation
High-speed NVMe for datasets
Full root access
Snapshots for checkpoints
DDoS-protected endpoints
24/7 specialist support

Plan comparison

Same software stack everywhere — pick by VRAM, TFLOPS and interconnect.

Feature	AI A100	AI H100	AI Cluster
GPU
GPU model	A100 80GB	H100 80GB SXM	4× H100 SXM
VRAM	80 GB HBM2e	80 GB HBM3	320 GB HBM3
FP16 compute	312 TFLOPS	1,979 TFLOPS	7,916 TFLOPS
NVLink interconnect		✓	✓
Compute
vCPU cores	16	26	96
System RAM	120 GB	200 GB	768 GB
Storage & network
NVMe Gen4 storage	1 TB	2 TB	8 TB
Network	10 Gbps	10 Gbps	25 Gbps
Software
CUDA + cuDNN pre-installed	✓	✓	✓
PyTorch & TensorFlow ready	✓	✓	✓
Docker + NVIDIA toolkit	✓	✓	✓
Management
Full root access	✓	✓	✓
Snapshots	✓	✓	✓
Arabic & English panel	✓	✓	✓
Dedicated engineer			✓

The hardware — NVIDIA A100 & H100

The GPUs the AI industry standardized on — dedicated to you, never fractioned, never time-shared.

NVIDIA A100 80GB The workhorse

80 GB HBM2e VRAM
312 TFLOPS (FP16)
2 TB/s memory bandwidth

Ideal for: fine-tuning, production inference, mid-scale training

NVIDIA H100 80GB SXM The frontier

80 GB HBM3 VRAM
1,979 TFLOPS (FP16)
3.35 TB/s memory bandwidth

Ideal for: large LLM training, multi-GPU clusters, research

What can you build?

Five workloads our GPU fleet runs every day — with the stack already in place.

LLM training

Train language models from scratch or continue from open weights like Llama and Mistral.

High-throughput inference

Serve models at production latency with batching and quantization tuned for HBM.

Fine-tuning

Specialize an existing model on your domain data — full fine-tune or parameter-efficient.

RAG pipelines

Embeddings, vector search and generation on one box — no cross-cloud latency.

Computer vision

Detection, classification and segmentation — from YOLO to diffusion models.

Benchmark performance on every metric

The numbers that decide your training time — counted, not promised.

0 TFLOPS FP16 — H100

0 GB VRAM per GPU

3.35 TB/s Memory bandwidth — H100

0 GB/s NVLink interconnect

0 MB/s NVMe dataset reads

Your stack is already installed

No driver hunting, no CUDA version hell — boot, activate the environment, start training.

CUDA + cuDNN The foundation for every GPU framework — version-matched to the driver. Pre-installed

PyTorch Pre-installed with full CUDA support — torch.cuda.is_available() is already true. Pre-installed

TensorFlow GPU support enabled out of the box, XLA ready. Pre-installed

Hugging Face Transformers + Diffusers — pull a model and run within minutes. Pre-installed

JupyterLab Research and experiment in the browser, against the real GPU. One command away

Docker + NVIDIA Container Toolkit for isolated, reproducible training environments. One command away

Storage & network at GPU speed

A starved GPU is a wasted GPU — the data path keeps up.

High-speed NVMe 7,000 MB/s Dataset reads never bottleneck your epoch time.

10Gbps+ network 10–25 Gbps Move checkpoints and datasets in minutes, not hours.

NVLink interconnect 600 GB/s GPU-to-GPU bandwidth that makes multi-GPU training scale.

Your models stay yours

Training data and weights are business secrets — the platform treats them that way.

Full GPU isolation

Your GPU is never shared, fractioned or time-sliced — training data stays inside your server.

DDoS protection

Exposed inference endpoints are shielded by 10Tbps+ network-layer filtering.

SSH key only

Key-based access from first boot — password login never even gets enabled.

Firewall rules

Control exactly who reaches your inference ports — managed from the panel.

GPU capacity in European regions

AI Servers currently deploy in Frankfurt and Helsinki — more regions as capacity lands.

FRA-1 Frankfurt, Germany Western Europe Live HEL-1 Helsinki, Finland Northern Europe Live

View all data centers

Teams shipping models on Hostrena

Fine-tuned Llama 3 70B on A100 in under 8 hours — the NVLink setup made multi-GPU training genuinely seamless.

Sarah Chen ML Engineer — Toronto

Running production inference at 10k requests/min — latency never exceeded 180ms even at peak.

Lukas Brandt AI Platform Lead — Berlin

Switched from a cloud giant — saved 35% monthly on our training runs with identical TFLOPS.

Priya Raman Research Scientist — Singapore

Frequently asked questions

Is CUDA pre-installed?

Yes — CUDA Toolkit and cuDNN ship pre-installed and version-matched to the driver. PyTorch and TensorFlow see the GPU on first boot.

What is the difference between A100 and H100?

H100 is the newer generation: ~6× FP16 compute (1,979 vs 312 TFLOPS), faster HBM3 memory (3.35 vs 2 TB/s) and a stronger transformer engine. A100 remains excellent value for fine-tuning and inference.

Can I use Docker with the GPU?

Yes — Docker plus the NVIDIA Container Toolkit are ready, so `--gpus all` works immediately for reproducible training environments.

How do I transfer large datasets to the server?

Over the 10–25 Gbps network via rsync/scp or object storage tooling. A 1 TB dataset lands in well under half an hour on the larger plans.

Can I connect multiple GPUs?

Yes — the AI Cluster tier wires 4× H100 over NVLink at 600 GB/s, tuned for distributed training. Talk to sales for larger fabrics.

Is Jupyter ready out of the box?

JupyterLab installs with one command and is documented in the panel quick-start — many teams run it behind the built-in firewall rules.

What VRAM is available per GPU?

80 GB HBM on both A100 (HBM2e) and H100 (HBM3) — enough for 70B-parameter inference with quantization, or training with model parallelism.

Is the GPU really dedicated?

Fully — no MIG fractions, no time-slicing, no sharing. The GPU appears in nvidia-smi as completely yours, because it is.

How do I store model checkpoints safely?

Snapshot the server at checkpoint milestones, or sync checkpoints off-box to object storage — both paths are documented in the panel.

Which regions have GPU capacity?

Frankfurt (FRA-1) and Helsinki (HEL-1) today — both GDPR-friendly EU regions. New regions are announced on the data centers page.

Can I run inference APIs publicly?

Yes — expose your ports behind the cloud firewall and DDoS protection. Many clients serve production traffic straight from their AI Server.

Is there an annual discount?

Yes — annual billing saves 15% on every GPU plan.

What does "price on request" mean for clusters?

Multi-GPU fabrics are sized with our engineers — GPUs, interconnect, storage and network are quoted as one tailored package.

Do you support spot/interruptible pricing?

Not yet — all GPU servers are reserved and persistent. For burst workloads, monthly billing with cancel-anytime keeps you flexible.

How fast can I start?

A100 and H100 servers are typically online within minutes of ordering; clusters are provisioned with an engineer within 1–2 business days.

Can I fine-tune open models like Llama or Mistral here?

That is the most common workload we see. A single A100 80GB fine-tunes 7B–13B parameter models comfortably with LoRA or QLoRA; full-precision tuning of larger models wants the H100 or a multi-GPU setup with NVLink. Datasets stage on local NVMe scratch space, checkpoints push to any S3-compatible storage, and the prebuilt PyTorch environment takes you from tokenizer to training inside the first hour.

Talk to our AI team about your workload

Start training your models today

CUDA ready, PyTorch installed, a GPU that is entirely yours. Minutes to first epoch — no setup marathon.

Choose your GPU server Talk to our AI team

A100 from $1,199/mo — annual billing saves 15%.