H200 combines Hopper with 141 GB HBM3e memory. It is worth evaluating for memory-heavy inference, long-context services and training configurations that need more room than an 80 GB GPU.
Hopper
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.
Reference price source ↗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 |
|---|---|---|---|
| Hyperstack ↗ | H200 SXM · 141 GB | A$ 5.53 | on-demand |
| Runpod ↗ | H200 · 141 GB | A$ 6.36 | secure-pod |
Published reference rates do not establish live availability or identical instance specifications. Your Arvica quote confirms the complete configuration and charges.
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.
Choose between H100 and H200 using completed-job cost or cost per successful request. More memory is useful only when your application can use it; compare B200 for a Blackwell software path.
NVIDIA specifies 141 GB HBM3e and 4.8 TB/s memory bandwidth. These are hardware specifications, not an application throughput guarantee.
Select a single GPU for workloads that fit locally; verify the node topology for tensor parallelism. Multi-node training additionally needs an explicit network and storage design.
Keep the model server, CUDA runtime and attention kernels compatible with Hopper. Test the intended context window under realistic concurrency rather than checking weights alone.
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 →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.
NVIDIA specifies 141 GB HBM3e and 4.8 TB/s memory bandwidth. These are hardware specifications, not an application throughput guarantee.
Keep the model server, CUDA runtime and attention kernels compatible with Hopper. Test the intended context window under realistic concurrency rather than checking weights alone.
Select a single GPU for workloads that fit locally; verify the node topology for tensor parallelism. Multi-node training additionally needs an explicit network and storage design.
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.
4 configurations for enquiry
