Infrastructure
Your team operates servers and network connections. Confirm the GPU configuration, model memory and any multi-server requirements.
Serve models on GPU servers you control.
Run open-weight models on Neverinstall Private Cloud with the built-in serving layer or your own model server. Reserve GPU capacity and set access for the teams using each endpoint.
Some prompts cannot leave your network.
Hosted model APIs receive every prompt and document your applications send. For internal records, that path needs its own review.
Connect applications to your model endpoint.
Your application calls a model endpoint running on your servers. You choose the model, serving software and which applications may reach it.
Your application
Calls
Your GPU server
Reserved GPUsFor Model Serving
Your modelBuilt-in Model Serving or your model server
Spare CPU and memoryHost sharingEarly access
- Models
Open-weight models and your own fine-tunes
- Serving
The built-in serving layer or your model server
- GPU capacity
Reservations and sharing modes matched to the card and workload
- Host CPU and memory sharing
Other workloads on spare capacity, in early access
Define server, model and data ownership.
Data paths
Review model files, prompts, application integrations and approved support access.
Measure one model under your workload.
Use your prompts, application traffic and GPU hardware.
Confirm hardware and model fit
Check the GPU model, driver, sharing mode and memory against the model. Review storage, networking and any multi-server GPU requirements.
Deploy the model
Deploy with the built-in serving layer or your own model server. Each deployment runs on reserved GPUs, behind an endpoint in your network.
Restrict access
Issue a key for each calling application and set team quotas. Review prompts, model files, integrations and support access together.
Measure and expand
Record latency, concurrency and GPU memory use, including long requests. Use the results to size additional models.
Related
Questions about private AI.
Do we have to use the built-in serving layer?
No. Run your model server as a Container or in a GPU Virtual Machine, with reserved GPUs, team quotas and the Audit Trail.
Which GPUs and sharing modes are supported?
NVIDIA GPUs support vGPU, MIG, time-slicing or passthrough, depending on the card. AMD GPUs support passthrough or SR-IOV partitions. The hardware review confirms the card, driver and mode for your workload.
Can a workload use GPUs across servers?
Yes, where the workload supports it. The platform can schedule work across GPU servers and support a workload that combines GPUs across servers. Review the model server, network and GPU configuration during sizing.
Where do models and prompts run?
Models and inference run on your servers. Review application data paths and any support access with your team. The deployment can run without an internet connection.
Does this cover training or host CPU and memory sharing?
This page covers Model Serving. Review training and fine-tuning capacity separately. Host CPU and memory sharing is in early access and needs its own pilot scope.
How is it priced?
GPU servers are quoted with Neverinstall Private Cloud, per node. Adding models or endpoints on the same licensed nodes adds no platform charge.
Size your first model deployment.
Tell us the model, serving software and GPU servers you plan to use. We will review the hardware and expected traffic with your team.