Model R&D
Scale from small experiments to large training runs, then release resources when training finishes.
Large-scale deployment, elastic scalingDedicated GPU clusters
Dedicated Cluster
Physically isolated, exclusive GPU clusters. Scale from a single machine to thousands of GPUs for distributed training, inference deployment and scientific computing.
Contact SalesDedicated Cluster Capabilities
Managed infrastructure with built-in observability, a complete development experience and research-grade performance
Sustained stability for weeks- Eliminate stragglers
- Predictable latency
Maintain high utilization throughout multi-week training runs and model deployments. Kernel, hardware, and storage acceleration reduce latency and ensure predictable performance.
- Validation testing
- Automated repair
- Node recovery
Seamlessly scale compute and storage from experimentation to production with high-speed network interconnect. Automatically maintain capacity online with continuous health checks, auto-repair, and self-service node replacement.
- Pre-configured dashboards
- Full-stack metrics
- Real-time alerts
Instantly monitor workloads with pre-configured Grafana dashboards and full-stack metrics covering GPU, storage, network, and Kubernetes. Gain complete system visibility without writing custom instrumentation code.
- CUDA version selection
- Multi-tool channels
Rapidly deploy clusters with pre-configured tools and optional driver and CUDA versions. Manage cross-team access via CLI, SDK, API, Terraform, or Web Console.
Use Cases
Compute for every stage of AI development
Inference Deployment
Deploy your own models with the capacity you need for low latency and high throughput.
Scientific Computing
High-performance compute for HPC simulations, data analytics, 3D rendering and more.