RTX 4090 provides Ada Lovelace compute with 24 GB GDDR6X. It is a candidate for development, image generation and memory-bounded inference experiments.
Ada Lovelace
Exact GPU variant, CPU, RAM and interconnect depend on the confirmed instance. Memory values are hardware references, not allocated resources.
Manufacturer specifications ↗Public reference / GPU-hour. Arvica sell price, taxes and additional services require an approved quote.
Reference price source ↗Choose on-demand or reserved compute. Location, start time and access environment are confirmed before provisioning.
| Published source | GPU variant | Reference / GPU-hour | Price basis |
|---|---|---|---|
| TensorDock ↗ | RTX 4090 · 24 GB | A$ 0.48 | host-starting |
Published reference rates do not establish live availability or identical instance specifications. Your Arvica quote confirms the complete configuration and charges.
Evaluate tasks whose weights, activations and caches fit within one GPU. For experiments, compare completed-job cost and reproducibility across available configurations.
Consider RTX 5090 for more memory and a Blackwell software path. For 48 GB workloads, compare RTX 6000 Ada and L40S before increasing GPU count.
The standard RTX 4090 has 24 GB GDDR6X. Model size alone does not account for batch size, attention cache and intermediate allocations.
Confirm the server's PCIe and networking layout for multiple GPUs. Separate graphics capability from any promised multi-node training capability.
Check Ada-compatible drivers and containers, plus application licensing. Request the exact OS and isolation configuration needed for your workflow.
NVIDIA hardware reference ↗ · Selection guidance is Arvica's assessment; validate it against your workload
Specify the OS, framework and container image version. Arvica confirms the driver and GPU compatibility before activation. A template name is not a software licence or a guaranteed preinstalled image.
Save an SSH public key during configuration. Keep the private key on your own device. The verified deployment record provides the actual host and login user once access is ready.
Separate boot disk, datasets, model weights and checkpoints in your capacity plan. Confirm persistence, backup ownership, attachment rules and deletion behaviour before relying on a volume.
Confirm ingress, egress, public IP access and private networking where required. For regulated or location-sensitive data, specify the required region in advanced settings before submitting.
Once Arvica supplies verified access, connect using your private key and the exact host/user shown in your instance. Then inspect the allocated GPUs and topology.
ssh -i ~/.ssh/your_key USER@HOST nvidia-smi nvidia-smi topo -m
Replace USER and HOST with your own deployment details. These commands inspect an existing machine; they do not provision one.
Parameter count × bytes per weight estimates weights only. Add KV cache, activations, temporary tensors and framework overhead. Training additionally needs gradients and optimiser state.
Record model version, precision, input/output length, concurrency, latency and error rate together. Compare the cost of a completed job or successful request under the same conditions.
Include all allocated GPUs, runtime, storage, networking, taxes and support. Monthly projections use 730 hours for comparison; your contract defines the billable period and minimum term.
Define your GPU allocation, interconnect, storage, location and commitment. Arvica prepares a capacity proposal around the workload.
Request capacity ↗Model fit and throughput depend on precision, software, batch size, context and system topology. Compare workload estimates rather than an unsupported tokens-per-second promise.
Training cost calculator →Evaluate tasks whose weights, activations and caches fit within one GPU. For experiments, compare completed-job cost and reproducibility across available configurations.
The standard RTX 4090 has 24 GB GDDR6X. Model size alone does not account for batch size, attention cache and intermediate allocations.
Check Ada-compatible drivers and containers, plus application licensing. Request the exact OS and isolation configuration needed for your workflow.
Confirm the server's PCIe and networking layout for multiple GPUs. Separate graphics capability from any promised multi-node training capability.
Do not assume so. Resource allocation, storage and reserved commitments may continue billing. Check the contract and request the appropriate lifecycle action through support.
Retention depends on the agreed storage configuration. Export data and confirm backup and volume retention before requesting termination.
Current configurations enter Arvica capacity review. Running status is shown only after infrastructure is verified.
No. Your quote defines GPU, storage, network, tax and service charges before you commit.
Use the enterprise capacity path for multi-node networking and reserved allocations.
Explore PCIe / SXM and virtual machine / bare-metal configurations. Reserve with Arvica before activation.
2 configurations for enquiry
