Serve models and share GPUs on your servers.

Allocate GPUs to models and Virtual Machines through Neverinstall Private Cloud.

Add Neverinstall Virtual Desktops for graphics applications. We review your cards, drivers and workloads before deployment.

Models, GPU Virtual Machines and graphics applications.

Model Serving

Deploy an open-weight model with the built-in serving layer, or bring your own model server on reserved GPUs.

GPU Virtual Machines

Assign a whole card or a supported GPU profile to a Virtual Machine. Confirm the mode against your card and workload in the hardware review.

GPU Desktops

Give CAD and rendering teams Windows or Linux desktops with GPU resources, delivered through Neverinstall Virtual Desktops.

Models and GPUs in the Console.

These product screens show Model Serving and GPU allocation with sample data. Measure latency, concurrency and memory use with your model and hardware during the pilot.

Open-weight models in the catalog, each with the verdict that it fits the cluster’s GPUs and how many concurrent requests or tokens of context it has room for.

Six model deployments on vLLM, including a 70B model on four L40S GPUs and smaller models on one or two.

A playground conversation in which a deployed model summarizes an internal policy circular for branch staff in four bullets.

Keys for one deployment, each named for the application that uses it, five active and one revoked.

Choose a mode supported by your hardware.

Choose a whole card, a supported partition or shared execution time. Available profiles, isolation and license requirements depend on the card and driver.

A whole card

Passthrough assigns a whole GPU to one Virtual Machine.

Partitions and profiles

MIG partitions supported NVIDIA cards into separate resources. vGPU provides profiles for Virtual Machines. Supported AMD cards use SR-IOV for GPU virtualization.

Shared execution time

Time-slicing lets workloads share GPU execution time. It does not provide MIG’s memory and fault isolation.

Choose a mode supported by your hardware.
Hardware	Modes to review	Before deployment
NVIDIA	vGPU, MIG, time-slicing and passthrough	Confirm the card, driver and supported profiles for your workload.
AMD	Passthrough and SR-IOV partitions	Confirm support on the specific card in the hardware review.
Across servers	Job scheduling or a workload spanning GPUs	Multi-server execution depends on the workload and interconnect. Review both before deployment.

Assign GPU resources and track allocations.

GPU server

GPUAllocated GPU resources

AI inference

Graphics workloads

CPU and memoryHost sharingEarly access

Virtual Machines

Containers

Illustrative allocation on one GPU server. Host CPU and memory sharing is in early access.

Assign resources

Share cards with a team and assign GPU resources to its model server, Virtual Machines or desktops.

Set access and limits

Apply team roles, quotas for each GPU profile and network rules for model endpoints.

Review allocations

Track reserved GPU resources by team, with history through resize, stop and restart. Allocation records do not measure GPU utilization.

Size the deployment with your workload.

Check the hardware
Review cards, drivers, GPU memory, storage and networking against the workload.
Deploy and set limits
Assign GPU resources, deploy the workload and configure team access and quotas.
Measure and size
Test latency, concurrency and memory use. Use the results to plan capacity and the quote.

GPU servers are quoted per node.

Your quote lists the GPU nodes, platform fees and required third-party licenses. Desktop delivery has a separate per-user license.

Platform: Per node, with Neverinstall Private Cloud
Desktop delivery: Neverinstall Virtual Desktops is licensed separately
Separate costs: Hardware and any required third-party licenses

Where teams start.

Evaluate in depth.

GPU allocation and host CPU and memory sharing.

Test how host sharing affects inference
Host CPU and memory sharing is in early access. Test usable host capacity and the effect on inference response times with your hardware and workloads.
Read the colocation research
Track GPU allocations
Track reserved GPU resources per team, including resize, stop and restart history.
Read the GPU metering research

GPU questions.

Do you host our models or see our prompts?

Models and inference run on your servers and network. Support access is agreed with your team first.

Can GPU workloads run without internet access?

Yes. Neverinstall Private Cloud can run offline. Agree on the model, driver and update requirements for the disconnected environment.

What does GPU metering record?

It records allocated GPU resources over time, including resize, stop and restart. Use workload measurements to assess utilization and inference performance.

Plan your GPU deployment.

Bring your GPU models, model or graphics workload and expected traffic. We will agree on the allocation, performance tests and capacity estimate for your hardware.