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.
CUDA-ready on first boot
From lightweight inference to massive LLM training — dedicated GPUs, never shared, never time-sliced.
Fine-tuning & inference
Serious model training
Multi-GPU NVLink fabric
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 GPUs the AI industry standardized on — dedicated to you, never fractioned, never time-shared.
Ideal for: fine-tuning, production inference, mid-scale training
Ideal for: large LLM training, multi-GPU clusters, research
Five workloads our GPU fleet runs every day — with the stack already in place.
The numbers that decide your training time — counted, not promised.
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
A starved GPU is a wasted GPU — the data path keeps up.
Training data and weights are business secrets — the platform treats them that way.
AI Servers currently deploy in Frankfurt and Helsinki — more regions as capacity lands.
Fine-tuned Llama 3 70B on A100 in under 8 hours — the NVLink setup made multi-GPU training genuinely seamless.
Running production inference at 10k requests/min — latency never exceeded 180ms even at peak.
Switched from a cloud giant — saved 35% monthly on our training runs with identical TFLOPS.
Yes — CUDA Toolkit and cuDNN ship pre-installed and version-matched to the driver. PyTorch and TensorFlow see the GPU on first boot.
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.
Yes — Docker plus the NVIDIA Container Toolkit are ready, so `--gpus all` works immediately for reproducible training environments.
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.
Yes — the AI Cluster tier wires 4× H100 over NVLink at 600 GB/s, tuned for distributed training. Talk to sales for larger fabrics.
JupyterLab installs with one command and is documented in the panel quick-start — many teams run it behind the built-in firewall rules.
80 GB HBM on both A100 (HBM2e) and H100 (HBM3) — enough for 70B-parameter inference with quantization, or training with model parallelism.
Fully — no MIG fractions, no time-slicing, no sharing. The GPU appears in nvidia-smi as completely yours, because it is.
Snapshot the server at checkpoint milestones, or sync checkpoints off-box to object storage — both paths are documented in the panel.
Frankfurt (FRA-1) and Helsinki (HEL-1) today — both GDPR-friendly EU regions. New regions are announced on the data centers page.
Yes — expose your ports behind the cloud firewall and DDoS protection. Many clients serve production traffic straight from their AI Server.
Yes — annual billing saves 15% on every GPU plan.
Multi-GPU fabrics are sized with our engineers — GPUs, interconnect, storage and network are quoted as one tailored package.
Not yet — all GPU servers are reserved and persistent. For burst workloads, monthly billing with cancel-anytime keeps you flexible.
A100 and H100 servers are typically online within minutes of ordering; clusters are provisioned with an engineer within 1–2 business days.
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.
CUDA ready, PyTorch installed, a GPU that is entirely yours. Minutes to first epoch — no setup marathon.
A100 from $1,199/mo — annual billing saves 15%.एक विषय चुनें और असिस्टेंट Hostrena के अपने दस्तावेज़ों से जवाब देगा।
जवाब AI असिस्टेंट की तरफ़ से आते हैं और गलत भी हो सकते हैं। किसी व्यक्ति से बात करना बस एक टैप दूर है।
एक विभाग चुनें और आपको तुरंत किसी व्यक्ति से जोड़ दिया जाएगा।
अभी कोई सपोर्ट विभाग उपलब्ध नहीं है। कृपया कॉन्टैक्ट फ़ॉर्म आज़माएं।
