NVIDIA GH200 Grace Hopper: Specs, Pricing & Rental. Rent GH200 GPU from $3.02/hr

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, 7x faster than PCIe Gen5. The coherent design means both CPU and GPU can access each other's memory directly 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 lets models spill 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 without issue 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.

GH200 is strongest when your workload actually uses the unified memory pool. For memory-bound LLM serving with very long contexts, graph neural networks on billion-edge datasets, or genomics pipelines with 100GB+ intermediate buffers, the combined 528GB CPU+GPU memory eliminates PCIe data copies and is often faster per dollar than stacking multiple H100s. For workloads that fit entirely in 80GB HBM, H100 SXM5 is cheaper per hour.

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.

Our platform is plug-and-play for standard deployments. For 100+ GPU clusters, you get dedicated support via Slack or Discord, plus sourcing assistance. Enterprise customers get dedicated support channels and SLA guarantees.

Dedicated GH200 instances are non-interruptible, run on a 99.99% SLA, and bill per-minute at the on-demand rate. Spot instances run on spare capacity at meaningfully lower rates but can be preempted when dedicated demand rises. Use spot for fault-tolerant workloads: batch inference, QLoRA fine-tuning with checkpointing every 15-30 minutes, or graph analytics jobs. Use dedicated for customer-facing inference endpoints or any job where an interruption would cost more than the savings. Both tiers live in the same control plane, so you can mix them across a project.