INFERX INFRASTRUCTURE

    Power your AI infrastructure.

    Deploy the InferX Runtime across the GPU infrastructure you operate—without building the scheduler, snapshot system, secure runtime, and serving platform yourself.

    Talk to our infrastructure team
    BUILDScheduler + runtime + isolation + model lifecycle + APIsDEPLOY INFERXOne production runtime across your GPU fleet

    THE INFRASTRUCTURE CHALLENGE

    Owning GPUs is not the same as operating an inference platform.

    01

    Idle capacity

    GPU fleets are provisioned for peaks, then billed through every quiet interval.

    02

    Low utilization

    Static model placement strands memory and compute across isolated serving stacks.

    03

    Slow activation

    Loading weights and initializing runtimes turns elasticity into a latency problem.

    04

    Runtime fragmentation

    Schedulers, serving engines, isolation, networking, and observability become separate platforms to maintain.

    The engineering burden sits between available GPU capacity and a production inference service.

    THE INFERX RUNTIME

    Model readiness without permanent GPU allocation.

    InferX restores initialized CPU + GPU runtime state, allocates GPU resources only while work is active, streams the response, and returns capacity to the pool.
    1. 01Request
    2. 02Scheduler
    3. 03GPU Snapshot Restore
    4. 04Allocate GPU Resources
    5. 05Stream Tokens
    6. 06Scale to Zero
    7. 07Runtime Snapshot Ready

    DEPLOYMENT OPTIONS

    One runtime. Your operating boundary.

    InferX adapts to the infrastructure model already in place instead of requiring workloads to move into a new public-cloud boundary.

    Kubernetes

    Run the InferX control plane and runtime across an existing cluster.

    Bare Metal

    Operate directly on GPU nodes where maximum hardware control is required.

    Private Cloud

    Keep models, traffic, and operations inside your cloud boundary.

    On-premises

    Deploy within enterprise or government data-center environments.

    GPU Cloud

    Add a differentiated inference layer across commercial GPU capacity.

    BUSINESS OUTCOMES

    The runtime changes the economics of the fleet.

    01GPU utilization

    Turn idle fleet capacity into active inference capacity.

    02Revenue per GPU

    Serve more workloads from the infrastructure already deployed.

    03Operating cost

    Reduce idle allocation and the platform work required to manage it.

    04White-label inference

    Offer an inference product under your own brand and operating boundary.

    05Scale-to-zero economics

    Separate model readiness from permanent GPU allocation.

    DEPLOYMENT ARCHITECTURE

    InferX becomes the inference layer inside your infrastructure.

    Your network, identity, hardware, and operating boundary remain yours. InferX supplies the control plane and runtime lifecycle between incoming demand and GPU execution.
    01
    Traffic boundaryYour API · Your identity · Your network
    02
    InferX control planeGateway · Scheduler · Placement · Lifecycle
    03
    Runtime stateCPU + GPU snapshots · Versioned model state
    04
    GPU executionSecure runtime · GPU slicing · NCCL isolation
    05
    InfrastructureKubernetes · Bare metal · Private cloud · On-prem
    Explore the complete architecture

    INFRASTRUCTURE PARTNERSHIPS

    Turn GPU capacity into an inference platform.

    Talk to our infrastructure team