A dedicated 8× B300 node is a complete single-tenant system for robotics, VLA, LLM training and sustained inference. Confirm the node architecture, tenancy, reserved term and deployment window in a formal proposal before activation.
No payment is required to submit a request.
An 8 × B300 node has 288 GB of HBM3e per GPU; the final system configuration is confirmed by proposal. NVLink/NVSwitch, CPU, RAM, NVMe, fabric, tenancy and delivery date are separate configuration decisions, not assumptions attached to the GPU name.
| Configuration | Best for | Pricing |
|---|---|---|
| 8 × B300 dedicated node | Robotics, VLA, LLM training | Reserved quote |
| VM allocation | Scoped pilot where supported | Request availability |
| Bare metal | Custom runtime and dedicated tenancy | Request availability |
| Multi-node cluster | Large-scale distributed AI | Custom proposal |
Current capacity, complete configuration and total pricing are confirmed before activation.
The node is procured as one system, so every component below is named in the proposal rather than left implicit in the GPU model.
A dedicated 8× B300 node is single-tenant: the complete system is reserved for one customer for the agreed term. It can be delivered as bare metal where supported, or as a VM allocation of the full node. State the preferred model in the request; the exact isolation model is written into the proposal.
Reserved terms for a dedicated node are typically 3, 6 or 12 months, with multi-year or custom structures for newly committed infrastructure. Lead time and the deployment window are confirmed per system and location in the proposal; this page is not a stock feed and does not state availability for any city.
Specify Sydney, Melbourne or another required location in your RFQ. Deployment location and availability are confirmed before commitment.
Reserved GPU capacity terms →How to compare a complete 8× B300 node quote →
One node is the practical starting point. When the workload justifies it, the same B300 node design scales into a multi-node cluster: 4 nodes (32 GPUs), 8 nodes (64 GPUs), 16 nodes (128 GPUs) and beyond, with the inter-node fabric, shared storage and operations model scoped in an enterprise proposal.
Share your model or framework, GPU count, preferred location and start date. For a cluster, include storage, interconnect and tenancy requirements if known.
English and Chinese assistance · Australia/APAC business-hours support · No charge before quote acceptance · Infrastructure provider disclosed before activation
One clear path from requirement to a deployable configuration.
Tell Arvica the workload, GPU count, region and start date.
Arvica confirms configuration, capacity and complete charges.
Run a pilot or proceed under a reserved-capacity agreement.
No. Each B300 has its own 288 GB of HBM3e; your framework and topology determine how model state is distributed across the eight GPUs. The final system configuration is confirmed by proposal.
State the preferred delivery model. Availability and the exact isolation model are confirmed in the proposal.
Yes. A dedicated node is reserved for one customer for the agreed term, and the tenancy and isolation model are written into the proposal and contract.
By adding nodes of the same design with an explicit inter-node fabric, shared storage and operations model: 32, 64, 128 or 512+ GPUs are scoped as an enterprise cluster proposal.
Lead time depends on the confirmed system and location and is stated in the proposal. Do not schedule a launch from a generic hardware roadmap.
Include framework, model, GPU count, deployment model, region, start date, duration and acceptance test.
Compare scope, allocation, storage, networking and pilot acceptance before committing to a B300 node. Includes a worked cost formula, not a supplier price.
Compare time to completion, communication behaviour and total GPU-hours before choosing a larger training configuration.
Download an editable worksheet to agree scope, compare configurations and decide whether to stop, adjust or scale. An evaluation method, not a customer case study.
Share the workload and Arvica will prepare deployable options for review.
