H200 SXM on Crusoe

Crusoe bills per-hour for GPU instances including NVIDIA H200 SXM. Hourly billing means you pay for full hours even if your job completes mid-hour. Plan your workloads accordingly to maximize cost efficiency.

Yes, Crusoe offers spot/preemptible instances for NVIDIA H200 SXM, which can reduce costs by 50-80% compared to on-demand pricing. Spot instances are ideal for fault-tolerant workloads like batch inference, hyperparameter tuning, and training jobs with checkpointing. Note that spot instances can be interrupted when demand is high, so ensure your workflow can handle preemption gracefully.

Crusoe provides access to NVIDIA H200 SXM instances via SSH, programmatic API, Docker containers. SSH access gives you full control over the instance for custom configurations and production deployments. API access enables automation and integration with your existing ML pipelines and CI/CD workflows.

Crusoe maintains SOC 2, GDPR certifications, making it suitable for regulated workloads. SOC 2 certification demonstrates strong security controls for handling sensitive data. Contact Crusoe directly for detailed compliance documentation and BAA agreements if needed.

Yes, Crusoe supports Kubernetes for orchestrating NVIDIA H200 SXM workloads. This enables you to deploy scalable ML pipelines, manage distributed training jobs across multiple GPUs, and integrate with MLOps tools like Kubeflow, Argo Workflows, and KServe. Kubernetes support is essential for teams building production-grade ML infrastructure.

The NVIDIA H200 SXM features 141GB of high-bandwidth memory, built on NVIDIA's Hopper architecture. As an enterprise-tier GPU, it's designed for large-scale AI training, inference at scale, and demanding HPC workloads. The substantial VRAM capacity supports large language models, complex neural networks, and multi-model deployments.

The NVIDIA H200 SXM on Crusoe is well-suited for large-scale AI/ML training, LLM fine-tuning, batch inference at scale, and high-performance computing workloads. Crusoe specifically excels at: Organizations with strict ESG mandates; Batch training workloads where carbon footprint is a key metric. Consider your model size, training data volume, and latency requirements when evaluating this combination for your specific use case.

Yes, Crusoe offers reserved instance pricing for NVIDIA H200 SXM, which can provide significant discounts (typically 20-40% off on-demand rates) for committed usage periods. Reserved instances are ideal for predictable, long-running workloads like production inference services, ongoing training pipelines, or development environments that run continuously. Contact Crusoe for current reserved pricing and commitment terms.

Crusoe differentiates itself with: Vertically integrated energy-to-cloud model; Use of stranded energy sources. These features may provide advantages depending on your specific workflow requirements and technical needs. Evaluate how these capabilities align with your ML infrastructure goals when making your decision.

To get started with NVIDIA H200 SXM on Crusoe, visit https://crusoe.ai?utm_source=gpuperhour&utm_medium=referral to create an account. Most providers offer a straightforward signup process, and some provide initial credits for new users. Once registered, you can typically launch a NVIDIA H200 SXM instance within minutes through their dashboard or API. We recommend starting with a small experiment to familiarize yourself with the platform before scaling up to larger workloads.