Choose inference infrastructure around your users and service targets, not just the name on the GPU.
No payment to enquire. Your region and GPU preferences carry into the request; capacity, pricing and start dates are confirmed in your quote.
Australia · Specify the Australian city, data location and deployment window.
For teams serving language models, RAG applications, vision models or private AI endpoints to users in Australia.
Share model and quantisation, context length, expected concurrency, peak traffic and latency targets. Distinguish first-token latency from sustained throughput.
Specify who owns the serving runtime, upgrades, autoscaling, monitoring and incident response. A GPU virtual machine alone does not include a managed inference endpoint.
Include model storage, vector databases, network access, backup location and recovery requirements. Ask for the proposed architecture and service terms for production workloads.
Review candidate configurations, full costs and proposed start dates before deciding.
Specify workload, budget, timeline and non-negotiable requirements.
Arvica reviews your requirement and prepares suitable compute options and pricing.
Check hardware, location, payment terms and service scope before placing an order.
That depends on traffic, isolation and latency requirements. Share average and peak demand so a dedicated or more flexible configuration can be evaluated.
No. Region is one factor; application design, network routing, model size and queueing also matter. Set measurable targets and validate them with representative traffic.
Review live on-demand prices and the storage, network, tax and service costs to include in your budget.
Estimate a budget around GPU count and runtime, then compare it with a confirmed quote.
Scope the term, start date and capacity commitments for sustained workloads.
