GH200 GPU Rental

The GH200 Grace Hopper Superchip integrates an ARM-based Grace CPU and a Hopper GPU into a single unified architecture connected via NVLink-C2C. Unlike H100 which relies on PCIe for CPU-GPU communication, GH200 provides 900 GB/s coherent interconnect bandwidth and 432GB of shared LPDDR5X memory accessible by both CPU and GPU. This makes GH200 ideal for workloads where data doesn't fit in GPU VRAM alone.

NVLink-C2C (Chip-to-Chip) is NVIDIA's high-bandwidth coherent interconnect that connects the Grace CPU and Hopper GPU within the GH200 module. It provides 900 GB/s bidirectional bandwidth, which is 7x faster than PCIe Gen5. The coherent nature means both CPU and GPU can access each other's memory seamlessly with hardware-managed cache coherency, eliminating the traditional PCIe bottleneck.

Yes, the GH200 is excellent for LLM inference. With 96GB of HBM3 GPU memory plus 432GB of LPDDR5X CPU memory accessible via NVLink-C2C, you can maintain massive KV caches for large context windows. The unified memory architecture allows models to seamlessly spill over from GPU to CPU memory without the PCIe bottleneck, making it ideal for serving large language models with long context lengths.

Workloads that benefit most from GH200's unified memory are those where data doesn't fit in GPU VRAM alone. This includes large graph neural networks with billion-edge graphs, genomics pipelines processing entire genomes, recommendation models with huge embedding tables, scientific simulations with large state spaces, and any AI workload that traditionally requires expensive CPU-GPU data transfers.

The Grace CPU uses ARM Neoverse V2 architecture. Most major ML frameworks including PyTorch, TensorFlow, and JAX have full ARM support and run natively. CUDA code runs on the Hopper GPU unchanged. Some CPU-dependent tools compiled for x86 may need recompilation for ARM, but NVIDIA provides optimized ARM containers and libraries. The vast majority of AI workloads run seamlessly on GH200.

Yes, the GH200 contains the same Hopper GPU architecture as the H100 with 96GB HBM3 memory. It's particularly well-suited for training models that require large memory, such as models with massive embedding tables or long sequences. However, for pure multi-GPU training throughput where InfiniBand scaling is critical, H100 with InfiniBand networking may be more cost-effective.

There's no minimum! Spheron charges by the hour with per-minute billing granularity. Rent a GH200 for just an hour to test your workload, or keep it running for months. You only pay for what you use with no long-term contracts or commitments.

The GH200 offers excellent price-performance for inference and memory-heavy workloads. At $1.88/hr, it provides 96GB GPU VRAM plus 432GB unified CPU memory, making it uniquely cost-effective for large dataset processing without CPU-GPU data transfer overhead. For workloads that can leverage the unified memory architecture, GH200 often delivers better total cost of ownership than traditional GPU-only solutions.

GH200 GPUs are currently available in US, Europe, and Canada regions. We're continuously expanding capacity and regions. Check the Spheron app for specific availability or contact our team for region-specific requirements.

Yes! We provide 24/7 technical support for all workloads. Our team has deep expertise in GPU infrastructure and can help with troubleshooting issues with GPU VM and bare metal servers. Enterprise customers get dedicated support channels and SLA guarantees.

Yes, Spheron offers Spot instances for GH200 at significantly reduced rates (up to 70% savings). However, Spot instances can be interrupted when demand increases. Key risks include: potential job interruption during training/inference, loss of unsaved state or checkpoints, and need to restart from last saved checkpoint. Best practices: implement frequent checkpointing (every 15-30 minutes), use Spot for fault-tolerant workloads, save model weights to persistent storage regularly, and consider Spot for development/testing rather than production inference. For critical production workloads, we recommend dedicated instances with SLA guarantees.