ARVICA / DOCUMENTATION

Arvica overview

Choose compute, prepare your deployment and manage the service through one workspace.

Your compute workspace

Arvica is the customer-facing AI compute platform and service layer. Browse GPU specifications, compare configurations and submit a deployment request. Your workspace brings together requests, quotes, orders, recorded usage and support.

From request to access

A catalogue entry describes a configuration, not a reserved machine. A request starts in review. Arvica confirms capacity, location, software, access and commercial terms before activation. Connection details appear when recorded for your instance.

Current release

The current workflow supports assisted provisioning. CPU and Spot self-service purchasing, standalone shared-volume provisioning and automatic provider lifecycle operations are not yet available. Payment integration remains in Stripe TEST mode.

Browse GPUs →

Create your account →Start with an email address and complete verification.Your first deployment request →Prepare a complete configuration before submitting.GPU and instance selection →Select memory and topology for the workload you will run.Deployment location →Confirm the actual location with the selected configuration.SSH and remote development →Connect only after instance access details are confirmed.Networking and access →Plan application ports and access controls with your instance.Images and GPU environments →Make the software stack part of your deployment requirements.Training and fine-tuning →Plan a reproducible training run and recovery path.Inference and image generation →Size the service around latency, throughput and memory.Storage and data retention →Understand what is requested, recorded and retained.Billing and credit →Read the confirmed contract and its transaction history.Cost planning and reservations →Compare total service cost rather than a GPU headline rate.Teams and account security →Keep access aligned with each person’s role.Customer API quick start →Use scoped keys against the environment where they were issued.Troubleshooting and support →Send enough context to make the issue reproducible.