Features
On-demand GPU instances (NVIDIA H100, A100, V100, etc.)
Pre-configured ML templates (PyTorch, TensorFlow, etc.)
Jupyter notebook hosting with auto-shutdown
Distributed training support
Model deployment as scalable API endpoints
Automatic versioning and experiment tracking
Team collaboration with private projects
Persistent storage with overage pricing
Per-second billing for cost efficiency
Multi-cloud and hybrid environment support (via Private Cluster)
1-click hosted notebooks with free GPUs
End-to-end MLOps on Gradient platform
Private cloud and on-premise deployment options
Low-latency desktop streaming (Portal, in preview)
Workflows (Beta) for distributed training pipelines
GPU compute with NVIDIA Vera Rubin, GB300, B300, Blackwell, Hopper, Ada Lovelace
CPU compute and bare metal servers
AI Object Storage with zero egress migration
Distributed file storage and dedicated VAST Storage
Backblaze multi-exabyte storage integration
Managed Kubernetes with automated provisioning
SUNK runtime acceleration for reinforcement learning
MLPerf v5.0 leading training and inference performance
CoreWeave Sandbox for model and agent development
Mission Control for observability, security, fleet lifecycle
Tensorizer for model optimization
Cluster Health Management and monitoring
Node lifecycle controller for automated node management
High-performance networking for cluster scale-out
Capacity plans with guaranteed compute and pricing
