GH200 brings a Grace CPU and Hopper GPU together through NVLink-C2C. Evaluate it for workloads that benefit from CPU–GPU data sharing and an Arm-compatible software stack.
On-demand prices are Arvica's live catalogue rates (USD per GPU-hour), shown at deploy time. Reserved and enterprise prices are set in an accepted Arvica proposal.
Term, volume and dedicated-capacity prices are set in an accepted Arvica proposal. Configuration, region and delivery are confirmed in your proposal.
Availability confirmed before order acceptance. Taxes and additional services are confirmed before payment.
Choose around your workload
01
When to choose this GPU
Consider memory-intensive analytics, scientific workloads and inference pipelines that move substantial data between host and GPU. Profile transfer time separately from compute time.
02
When to consider alternatives
Compare H200 when your application requires an x86 host environment. A GH200 configuration requires validation of host-native dependencies, not just the GPU framework.
03
Memory planning
NVIDIA documents 96 GB HBM3 and 144 GB HBM3e variants. CPU memory is a separate resource; confirm both allocated capacities and the exact system variant.
04
Interconnect and scale
NVLink-C2C connects CPU and GPU within a Superchip. Multi-GPU and multi-node communication require their own confirmed topology.
05
Software compatibility
Check Arm64 container images, compiled extensions and data libraries before migration. Use a representative dataset to validate memory placement and host–device transfers.
Specify the OS, framework and container image version. Arvica confirms the driver and GPU compatibility before activation. A template name is not a software licence or a guaranteed preinstalled image.
SSH & private access
Save an SSH public key during configuration. Keep the private key on your own device. The verified deployment record provides the actual host and login user once access is ready.
Storage & checkpoints
Separate boot disk, datasets, model weights and checkpoints in your capacity plan. Confirm persistence, backup ownership, attachment rules and deletion behaviour before relying on a volume.
Network & data location
Confirm ingress, egress, public IP access and private networking where required. For regulated or location-sensitive data, specify the required region in advanced settings before submitting.
First connection checklist
Once Arvica supplies verified access, connect using your private key and the exact host/user shown in your instance. Then inspect the allocated GPUs and topology.
Replace USER and HOST with your own deployment details. These commands inspect an existing machine; they do not provision one.
Size the job before committing
Model memory
Parameter count × bytes per weight estimates weights only. Add KV cache, activations, temporary tensors and framework overhead. Training additionally needs gradients and optimiser state.
Measure useful throughput
Record model version, precision, input/output length, concurrency, latency and error rate together. Compare the cost of a completed job or successful request under the same conditions.
Complete cost
Include all allocated GPUs, runtime, storage, networking, taxes and support. Monthly projections use 730 hours for comparison; your contract defines the billable period and minimum term.
Model fit and throughput depend on precision, software, batch size, context and system topology. Compare workload estimates rather than an unsupported tokens-per-second promise.
Consider memory-intensive analytics, scientific workloads and inference pipelines that move substantial data between host and GPU. Profile transfer time separately from compute time.
What should I check about memory?
NVIDIA documents 96 GB HBM3 and 144 GB HBM3e variants. CPU memory is a separate resource; confirm both allocated capacities and the exact system variant.
What software should I prepare?
Check Arm64 container images, compiled extensions and data libraries before migration. Use a representative dataset to validate memory placement and host–device transfers.
What interconnect is included?
NVLink-C2C connects CPU and GPU within a Superchip. Multi-GPU and multi-node communication require their own confirmed topology.
Can I stop billing by shutting down the operating system?
Do not assume so. Resource allocation, storage and reserved commitments may continue billing. Check the contract and request the appropriate lifecycle action through support.
Is my data retained after termination?
Retention depends on the agreed storage configuration. Export data and confirm backup and volume retention before requesting termination.
Can I deploy immediately?
Current configurations enter Arvica capacity review. Running status is shown only after infrastructure is verified.
Is the live GPU-hour price my total cost?
Not necessarily. On-demand usage is billed at Arvica's live catalogue rate; storage, network, tax and service charges are itemised before you commit, and reserved or enterprise terms are set in your proposal.
Can I use a larger cluster?
Use the enterprise capacity path for multi-node networking and reserved allocations.
Consider memory-intensive analytics, scientific workloads and inference pipelines that move substantial data between host and GPU. Profile transfer time separately from compute time.
Evaluate H100 for repeatable training jobs and inference services with established CUDA kernels. Benchmark your actual sequence lengths and batch sizes before reserving a larger allocation.
Test H200 when model weights, KV cache and concurrent requests compete for memory. Extra capacity can reduce the need to split a workload across GPUs, depending on precision and runtime overhead.
Consider this platform for distributed training or inference that benefits from a large NVLink domain. Start with the model parallelism strategy, dataset and expected utilisation.
What changes between configurations
Configuration
Practical difference
Confirm before ordering
Grace Hopper Superchip · Bare Metal · 96 / 144 GB HBM3 / HBM3e
Dedicated physical host configuration; host access follows the confirmed service
Exact card, GPU allocation, peer-to-peer connectivity, CPU and disk limits
Size the working set
Weights alone are approximately parameters × bytes per parameter. A 7B model at BF16 needs about 14 GB just for weights; a 70B model about 140 GB. Add KV cache, activations, framework buffers and training state. These are arithmetic estimates, not fit guarantees.
Measure useful performance
Compare the same model revision, precision, sequence length and concurrency. Record time to first token, p95 latency, output tokens/s, peak memory and full instance cost. Training comparisons should include step time and convergence at the same quality target.
Evidence status
Manufacturer specifications are linked below. No Arvica application-throughput benchmark is published for this configuration yet. Request a workload-specific acceptance test before making performance commitments.
Configure dedicated PCIe / SXM, virtual-machine and bare-metal capacity with Arvica. Submit your requirements for a deployment proposal.
Request your preferred configuration. Host sizes are illustrative; the written proposal confirms capacity, location, final pricing and delivery. Submitting an enquiry is free and does not reserve capacity.
1 configurations available for enquiry
Capacity confirmation requiredBare metal
GH200 Grace Hopper Superchip
Price in your proposalYour configuration and term are priced in an Arvica proposal. Delivery schedule confirmed in proposal.
Illustrative configuration
GPU
1 × GH200
GPU memory
96 / 144 GB HBM3 / HBM3e
vCPU
16
RAM
128 GB
Storage
500 GB
AvailabilityConfirmation requiredRegionConfirm in proposalServiceArvica Cloud
