GPU infrastructure for training and inference. Run hourly workloads, reserve dedicated 8-GPU nodes, or plan enterprise clusters from 32 to 512+ GPUs across Australia, APAC and global regions. Reserved capacity, location and delivery are confirmed in your proposal.
Tell us the GPU, quantity, region and start date. We'll reply with availability and pricing.
Explore currently quoteable GPU configurations. Review the exact variant, live capacity and price on the configuration page.
General training, fine-tuning and inference
US$3.99 / GPU-hour · Limited
H100 SXM 80GB · 80 GB
Larger-memory training and large-model inference
From US$3.79 / GPU-hour · Limited
H200 NVL 143GB · 143 GB
2 configurations available. See all →
Blackwell Ultra high-end compute and dedicated scale-up
Request dedicated or reserved capacity for this GPU family.
Bring your model, data and environment.
Confirm capacity and prepare GPU resources.
Run your workload across GPU compute cores.
Retrieve outputs and continue building.
Concept animation · not live telemetry
Choose hourly instances, complete dedicated physical nodes or a committed enterprise cluster. H200, B200 and B300 capacity supports training and inference, with dedicated and enterprise configurations confirmed through a capacity proposal.
Typically 1–8 GPUs, billed hourly or by usage. Review the current configuration, capacity and rate before deployment.
Configure hourly GPUs →Complete single-tenant physical systems with H200, B200 or B300. Reserve one or more nodes for an agreed term, with configuration and capacity confirmed before commitment.
Request dedicated capacity →32 / 64 / 128 / 512+ GPUs with multi-month or multi-year commitments. Architecture, capacity, location, delivery and SLA are agreed in the enterprise proposal.
Request enterprise capacity →Bring your own GPU servers. Planned Australian high-density, liquid-cooled colocation with expressions of interest now open for 50 kW–500 kW+ blocks.
GPU colocation in Australia →Finance, deploy and operate dedicated GPU infrastructure with Arvica.
Learn More →Arvica Finance: Financing options for eligible business customers, subject to Arvica credit assessment, approval and final terms.
Dedicated 8-GPU nodes for robotics, VLA, LLM training and large-scale AI. Scale to multi-node capacity under a confirmed deployment proposal.
Arvica structures dedicated and committed GPU capacity through its infrastructure network across Australia, APAC and global regions. Your reservation proposal confirms the deployment location, technical configuration, service responsibilities and delivery schedule.
Explore deployment regions. Exact availability and location are confirmed when you configure your GPU.
Validate simulation, perception and robot learning with a scoped GPU pilot before scaling.
Explore configurations and the pilot →Size an inference deployment around response time, concurrency and cost per useful result.
Explore configurations and the pilot →Run a representative training pilot, measure memory and step time, then plan the full run.
Explore configurations and the pilot →Plan GPU capacity for vision recognition, video generation, rendering and immersive workloads around memory, pipeline and delivery requirements.
Explore configurations and the pilot →Select GPU families and quantities without a separate supplier search.
Keep deployment requests, access information and resources together.
Review recorded usage, contract balances and invoices in one place.
Arvica coordinates your service request and technical escalation.
A direct path to compute, with the details you need at every stage.
Go to platform ↗GPU model and quantity
Environment, storage and SSH
Capacity, pricing and terms
Deployment, usage and billing
Specify CUDA, PyTorch or your container requirements when configuring compute.
Explore →Use the GPU Advisor, then continue to the same configuration flow.
Explore →Explore workload assumptions and compute costs before you commit.
Explore →From GPU selection to access, Arvica guides you through configuration, capacity confirmation and deployment.
Deploy GPU in 4 steps
Choose a GPU model and quantity, then specify storage, environment and access. Arvica confirms the deliverable instance specification before activation.
A configuration with confirmed live capacity deploys automatically. Configurations that still need assisted activation say so before you submit, with Arvica operations confirming capacity and provisioning the approved order.
Connection details appear in your deployment after the real resource has been provisioned and verified. A pending request does not have a live IP.
Specify your environment in Configure. Image, driver and framework compatibility must be confirmed for the selected instance; only validated templates are selectable.
Arvica confirms the GPU configuration, storage, network, services, taxes and billing units in the accepted quote before charges start.
Lifecycle actions and billing differ by instance. Storage and other charges may continue. Arvica confirms stop and termination terms before activation.
A GPU instance is one instance on one node, with one or more supported GPUs. A cluster is several coordinated nodes with verified networking. Node count, GPUs per node and total GPUs are always shown separately.
Contact sales@arvica.ai to discuss capacity and data residency, or support@arvica.ai for an existing deployment. Dedicated and enterprise proposals confirm the deployment location, service responsibilities and contractual service levels.
