Evaluate GPU cloud costs for Singapore-based AI teams, from H200 inference to multi-GPU training. Request a Singapore deployment or compare an Australian deployment where your latency and residency requirements allow it.
Serving customers across Asia and globally. Australia and Singapore are deployment options to discuss; the actual location, availability and delivery date are confirmed in your quote. A translated page does not imply local GPU stock.
GPU selection
NVIDIA H100
Memory per GPU
80 GB · PCIe / SXM
Public reference / GPU-hour · AUD
A$ 3.12 – A$ 5.94
A starting point for training and inference when the model fits the selected memory configuration. PCIe and SXM differ in memory bandwidth and multi-GPU connectivity; specify the exact variant.
Consider for memory-intensive inference, larger KV caches and models that outgrow an 80 GB device. Compare the full instance configuration, rather than memory alone.
Consider for larger training and inference workloads using Blackwell-compatible software. Confirm numerical precision, GPU count, NVLink topology and framework versions.
Discuss dedicated multi-GPU capacity for large models, robotics and simulation. B300 and GB300 are different system configurations; the quote must identify which is supplied.
Register interest in Rubin. This is an enquiry about future capacity, not an immediately purchasable instance. Pricing and availability require confirmation.
Estimate GPU count × hourly rate × billable hours. Then add persistent storage, snapshots, data transfer, public IPs and any managed services. Ask whether resources keep billing when the GPU is stopped.
Worked example, not an offer: 8 GPUs at A$5 per GPU-hour for 100 hours = A$4,000 for GPU time, before other services and tax. Reserved commitments may use different billing terms.
Before ordering, confirm minimum rental term, billing granularity, cancellation terms, included storage and transfer allowances, overage rates, and whether prices include GST or other applicable taxes.
These are dated public market references converted to AUD, not an Arvica offer or proof of stock. They may cover different instance variants. Your quote confirms the complete configuration, applicable taxes and billing units.
A first estimate for model weights is parameter count × bytes per parameter. For 70 billion parameters, FP16/BF16 weights alone require about 140 GB (decimal); 4-bit weights start around 35 GB before quantization metadata. KV cache, activations and runtime overhead require additional memory.
Weights only · GB (decimal)
Model parameters (billions)
FP16 / BF16
INT8
4-bit
8
16
8
4
32
64
32
16
70
140
70
35
405
810
405
202.5
These calculations are not performance benchmarks. Context length, batch size, concurrency, precision and training method change memory needs. Training also needs gradients and optimizer state. Share those settings so the configuration can be checked.
Give us the details that change the recommendation
Include model name and size, training or inference, precision, context length, expected concurrency, dataset size, storage, network requirements and the software image. If you need a specific country for data residency, state it explicitly.
For an interactive inference service, measure latency from your actual Singapore users and data sources. Include peak concurrency, context length and time-to-first-token targets. For batch training, compare dataset transfer time, checkpoint storage and total job cost instead of relying on hourly GPU price alone.
If Singapore hosting is mandatory, specify Singapore for compute, persistent data and backups separately. We will confirm the available location in writing; this page does not promise Singapore inventory. Ask for the invoicing entity, currency, tax treatment and data-transfer charges before accepting the quote.
The order should name the compute location, storage location, backup location and permitted support access. Serving customers in a country is different from hosting in that country. Request these details before transferring data.
How do I get support and when will I receive a quote?
Contact support@arvica.ai or submit the form below. Arvica reviews the configuration, capacity and commercial terms before quoting. Ask for an expected response and delivery date; no fixed quote turnaround or activation time is promised on this page.
Which GPU is fastest for my model?
A useful comparison uses the same model, precision, batch size, context length, software version and GPU count. Ask for verified measurements under those conditions. This guide does not claim unmeasured performance or publish invented customer results.
Can I use a purchase order or reserved capacity?
Include your legal entity, purchase order reference, requested term and billing requirements. Arvica confirms the contract, deposit, invoicing and payment terms in the quote. Submitting this form does not charge your card or provision a resource.
Can customers in mainland China use the service?
Check page access, form submission, SSH and model download paths from the actual customer network before ordering. Overseas availability does not prove mainland connectivity. Confirm data transfer and access requirements for the intended workload.
Send your requirements
Tell us the workload and deployment constraints. Arvica will prepare suitable configuration options and pricing. You can write your requirement in the language used on this page.
