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
Reverse-engineer causal mechanisms of AI models
Reveal internal structure and hidden representations
Detect performative chain-of-thought in LLMs
Identify confounders and debug model behavior
Validate whether models learned real clinical understanding
Trace unstable behaviors to brittle internal features
Reduce hallucinations via features as rewards
Accelerate materials discovery with self-correcting search
Control training precisely with less data and off-target effects
Support for LLMs, life sciences, and robotics/vision models
Harvest activations from trillion-parameter models
SOC 2 Type II certified security and compliance
Analyze latent policy structure in robotics models
Interpret genomic models like Evo 2
Discover novel biomarkers via model reverse-engineering
