Rent dedicated GPU hardware for training and inference with monthly hosting, no KYC and crypto payment. Choose the available configuration for your model, dataset and expected concurrency; compare the complete workload cost before choosing a billing model.
What the hardware looks like
- NVIDIA RTX 4090, or L4 and L40S cards with 24 GB and 48 GB of VRAM
- Dual AMD EPYC CPUs, 32 cores, with 64 GB to 256 GB of RAM
- NVMe storage, with 10 Gbps to 25 Gbps uplinks
- 100 TB of bandwidth, which matters when you are moving datasets and weights
Why rent bare metal for this
The GPU is yours for the month
The GPU is attached to your dedicated machine. You control the workload schedule and software stack; confirm the plan, availability and included resources before ordering.
Your data and your weights stay put
Keep training data and model weights on hardware you administer. External model APIs, telemetry and connected tools can still receive data when enabled, so review the software configuration as well as the hosting location.
Order it the same way as everything else
No KYC, no email required, and payment in Bitcoin, Monero, USDT or TRX. See no-KYC hosting.
Pair the hardware with Ollama and Open WebUI for a chat interface backed by your own models. Test GPU compatibility, model size, context length and concurrent requests before committing to a production target.
VRAM is the constraint that decides whether a model runs at all. A 24 GB card and a 48 GB card are different classes of machine, and quantisation only buys so much. Tell us the models you intend to serve and we will point at the right configuration rather than the biggest one.
GPU stock is the least predictable part of our range. Ask us what is currently free before you plan a launch date around a specific card.
Plan inference, checkpoints and recovery
Confirm GPU access and runtime compatibility before deploying containers. The presence of an Impreza Agent does not by itself configure GPU drivers or make every model fit in VRAM. For supported app deployments with the Agent online, use app inspection and maintenance to inspect deployment health alongside your model-specific checks.
Map datasets, trained weights and checkpoints separately from application configuration. Review the covered paths before using app backups to Impreza S3; a deployment backup is not a whole-machine image or an automatic copy of every training dataset. Keep a recoverable copy of valuable checkpoints outside the machine.
Before moving a workload, confirm the destination GPU, available memory and data-transfer requirements. Test model loading and a representative request before moving inference traffic.
Start now
Browse offshore dedicated servers or ask us which GPU configuration fits your workload.