GB200 NVL72 combines Grace CPUs and Blackwell GPUs in a liquid-cooled rack-scale system. This is a capacity engagement covering the full system, not a single-GPU rental.
Grace Blackwell
Exact GPU variant, CPU, RAM and interconnect depend on the confirmed instance. Memory values are hardware references, not allocated resources.
Manufacturer specifications ↗Public reference / GPU-hour. Arvica sell price, taxes and additional services require an approved quote.
Choose on-demand or reserved compute. Location, start time and access environment are confirmed before provisioning.
| Published source | GPU variant | Reference / GPU-hour | Price basis |
|---|---|---|---|
| No comparable published rate is currently recorded for this configuration. Request a configuration-specific quote. | |||
Published reference rates do not establish live availability or identical instance specifications. Your Arvica quote confirms the complete configuration and charges.
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.
Compare GB300 for Blackwell Ultra requirements. A B200 node may be easier to size when the workload does not need a full rack-scale allocation.
The NVL72 design includes 72 Blackwell GPUs and 36 Grace CPUs. Distributed software must still manage memory placement and communication across devices.
Specify the NVLink domain, external network, storage throughput, liquid-cooling environment and acceptance tests together. An aggregate bandwidth figure is not per-GPU application bandwidth.
Grace uses Arm CPUs. Verify that containers, native libraries, monitoring agents and data tooling support the host architecture before migration.
NVIDIA hardware reference ↗ · Selection guidance is Arvica's assessment; validate it against your workload
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.
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.
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.
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.
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.
ssh -i ~/.ssh/your_key USER@HOST nvidia-smi nvidia-smi topo -m
Replace USER and HOST with your own deployment details. These commands inspect an existing machine; they do not provision one.
Parameter count × bytes per weight estimates weights only. Add KV cache, activations, temporary tensors and framework overhead. Training additionally needs gradients and optimiser state.
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.
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.
Define your GPU allocation, interconnect, storage, location and commitment. Arvica prepares a capacity proposal around the workload.
Request capacity ↗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.
Training cost calculator →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.
The NVL72 design includes 72 Blackwell GPUs and 36 Grace CPUs. Distributed software must still manage memory placement and communication across devices.
Grace uses Arm CPUs. Verify that containers, native libraries, monitoring agents and data tooling support the host architecture before migration.
Specify the NVLink domain, external network, storage throughput, liquid-cooling environment and acceptance tests together. An aggregate bandwidth figure is not per-GPU application bandwidth.
Do not assume so. Resource allocation, storage and reserved commitments may continue billing. Check the contract and request the appropriate lifecycle action through support.
Retention depends on the agreed storage configuration. Export data and confirm backup and volume retention before requesting termination.
Current configurations enter Arvica capacity review. Running status is shown only after infrastructure is verified.
No. Your quote defines GPU, storage, network, tax and service charges before you commit.
Use the enterprise capacity path for multi-node networking and reserved allocations.
Explore PCIe / SXM and virtual machine / bare-metal configurations. Reserve with Arvica before activation.
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