Train machine learning models at any scale. From single-GPU experiments to multi-node distributed training with the latest NVIDIA A100 and H100 GPUs.
Choose the right GPU configuration for your training workload.
Prototyping & light training
Production model training
Large language models
Frontier AI & distributed training
Scale across multiple GPUs and nodes with PyTorch DDP, Horovod, and DeepSpeed.
JupyterLab and VS Code with GPU support, pre-installed libraries, and team collaboration.
Automatic logging of metrics, hyperparameters, and model artifacts with MLflow integration.
400 Gbps InfiniBand for multi-node training with near-linear scaling efficiency.
Automated data preprocessing and augmentation pipelines with versioning.
Save up to 80% with interruptible training jobs and automatic checkpointing.
With CUDA 12, cuDNN 8
With GPU acceleration
XLA compilation
Transformers & Diffusers
ZeRO optimizer
Distributed training
Train large language models with multi-GPU clusters
Image classification, object detection, segmentation
Train diffusion models, GANs, and VAEs
Train RL agents with parallel environments
ASR, TTS, and music generation models
Fine-tune foundation models on your data