GPU Cloud

NVIDIA accelerators at scale.

H200, H100, and L4 GPUs on demand. Purpose-built for ML training, LLM inference, 3D rendering, and scientific computing. NVLink and InfiniBand for multi-node scaling.

HGX H200 8-GPU SUPERPODNVSWITCH FABRIC900 GB/s Bi-DirectionalGPU 0GPU 1GPU 2GPU 3GPU 4GPU 5GPU 6GPU 7400G INFINIBAND NETWORK LEAF SWITCHESNVIDIA H2001.1 TB HBM3eNVLink 5.0900 GB/s Switch

Latest GPUs

NVIDIA H200

900 GB/s

NVLink 4.0

400 Gbps

InfiniBand

MIG support

Isolation

Machine families

Purpose-built configurations for every workload profile — from web serving to GPU-accelerated ML training.

A4 / H200

H200 Instances

Latest-generation NVIDIA H200 Tensor Core GPUs with 141 GB HBM3e memory. Ideal for the largest LLM training runs and real-time generative AI inference.

LLM trainingGenerative AIScientific HPCMulti-modal models
View all configurations

GPUs

1 – 8 × H200

GPU Memory

Up to 1.1 TB HBM3e

NVLink

5.0

Network

400G InfiniBand

Engineered for AI.

Purpose-built GPU infrastructure from single-GPU inference to multi-node training.

Tensor Core acceleration

4th and 5th gen Tensor Cores with FP8, FP16, BF16, and TF32 for mixed-precision training and inference.

NVLink & InfiniBand

Up to 900 GB/s GPU-to-GPU bandwidth. 400G InfiniBand for multi-node scaling across thousands of GPUs.

Multi-Instance GPU (MIG)

Partition a single GPU into up to 7 isolated instances. Run multiple inference models on one GPU securely.

High-bandwidth storage

Local NVMe SSDs and parallel file systems for training data. No I/O bottleneck for large dataset workloads.

Confidential GPU computing

Hardware-based GPU memory encryption. Train on sensitive data without exposing it to the cloud operator.

Spot GPU instances

Access GPU capacity at up to 70% discount for fault-tolerant training jobs with automatic checkpointing.

Getting started

Launch your first instance in three steps. CLI, console, or API — your choice.

Terminal
ur compute instances create gpu-training \
  --machine-type=a3-highgpu-8g \
  --accelerator=type=nvidia-h100,count=8 \
  --image-family=deep-learning-vm \
  --zone=eu-west1-b

Accelerate every workload.

From foundation model training to real-time inference — GPU instances for every scale.

Train foundation models at scale

Multi-node H100/H200 clusters with NVLink and InfiniBand. Train 70B+ parameter models with FSDP, DeepSpeed, or Megatron-LM. Automatic checkpointing for spot preemption.

View tutorial

Suggested configuration

8× A3-highgpu-8g · 64× H100 · InfiniBand

Estimate your costs

Create detailed configurations to see exactly how much your architecture will cost. Pay for what you use, down to the second.

Configuration 1

Estimated: $3443.00/mo

GPU Platform

GPUs

Compute Shape

Storage & Network

Local NVMe SSDsHigh-speed scratch space for datasets
InfiniBand Networking400 Gbps inter-node connectivity

Pricing Strategy

Config 1 cost$3443.00

Cost details

$3443.00

GPU prices are hourly but calculated as monthly estimates here. Spot clusters are subject to preemption.

Configuration 1
$3443.00
1× H100 GPUs$2679.10
Compute Resources$751.90
Storage & Network$12.00

Works seamlessly with

Model Training Studio
Kubernetes Engine
Cloud Storage
Cloud Monitoring
MLOps Pipeline
IAM
Cloud Logging
Artifact Registry

Frequently asked questions

Ready to accelerate?

Launch your first GPU instance in minutes. New accounts receive GPU credits.