NVIDIA H200 GPU Australia — Cloud, Dedicated & Reserved
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.
Request H200 in Australia and APAC as hourly cloud GPUs, a dedicated 8-GPU H200 node or reserved capacity. With 141 GB HBM3e per GPU on the Hopper platform, H200 keeps mature CUDA, TensorRT-LLM and PyTorch workloads on a proven runtime while adding memory for long-context inference, fine-tuning and training. GPU cloud infrastructure in Australia · enterprise GPU clusters in Australia
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.
Choose on-demand or reserved compute. Location, start time and access environment are confirmed before provisioning.
AUSTRALIA & APAC / H200
H200 GPU compute for dedicated and enterprise workloads
Request H200 for on-demand evaluation, a dedicated 8-GPU node, reserved capacity or a multi-node enterprise cluster. Arvica confirms the exact system, Australia/APAC region, interconnect, storage, term, current capacity and pricing before commitment. GPU compute for Australia.
H200 is the Hopper-generation choice when the software stack is already validated: existing CUDA images, attention kernels and serving stacks run without a platform migration, and the extra memory over an 80 GB GPU reduces the need to shard a model across cards. Dedicated 8-GPU H200 nodes and reserved H200 capacity are quoted as complete configurations for Australia and APAC.
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
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.
02
When to consider alternatives
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.
03
Memory planning
NVIDIA specifies 141 GB HBM3e and 4.8 TB/s memory bandwidth. These are hardware specifications, not an application throughput guarantee.
04
Interconnect and scale
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.
05
Software compatibility
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.
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.
Yes. Request a dedicated 8-GPU H200 node or reserved H200 capacity with the required location, term and start date. Location, configuration and pricing are confirmed in a written proposal; the request itself does not reserve capacity.
What is the difference between hourly and dedicated H200 capacity?
Hourly access runs on live cloud capacity billed per GPU-hour in USD. Dedicated capacity is a single-tenant allocation or complete node reserved for your term, with host, storage, network and support defined in the proposal.
How do I choose the GPU count?
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.
What should I check about memory?
NVIDIA specifies 141 GB HBM3e and 4.8 TB/s memory bandwidth. These are hardware specifications, not an application throughput guarantee.
What software should I prepare?
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.
What interconnect is included?
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.
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.
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.
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.
Evaluate B200 for workloads with a Blackwell-compatible software stack and a clear need for low-precision acceleration. Measure quality as well as throughput when changing precision.
Evaluate B300 when model state, activations or KV cache constrain your current deployment. Use a representative model and concurrency target to decide whether fewer larger GPUs improve total cost.
What changes between configurations
Configuration
Practical difference
Confirm before ordering
PCIe NVL · VM · 141 GB HBM3e
Guest OS and assigned resources within a virtual machine
Exact card, GPU allocation, peer-to-peer connectivity, CPU and disk limits
PCIe NVL · Bare Metal · 141 GB HBM3e
Dedicated physical host configuration; host access follows the confirmed service
Exact card, GPU allocation, peer-to-peer connectivity, CPU and disk limits
SXM · VM · 141 GB HBM3e
Guest OS and assigned resources within a virtual machine
GPU count, NVLink/NVSwitch topology, inter-node fabric and NUMA placement
SXM · Bare Metal · 141 GB HBM3e
Dedicated physical host configuration; host access follows the confirmed service
GPU count, NVLink/NVSwitch topology, inter-node fabric and NUMA placement
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.
4 configurations available for enquiry
Capacity confirmation requiredVirtual machine
H200 PCIe NVL
Price in your proposalYour configuration and term are priced in an Arvica proposal. Delivery schedule confirmed in proposal.
Illustrative configuration
GPU
1 × H200
GPU memory
141 GB HBM3e
vCPU
16
RAM
128 GB
Storage
500 GB
AvailabilityConfirmation requiredRegionConfirm in proposalServiceArvica Cloud
