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Run:ai adds an AI-aware control and scheduling layer to Kubernetes; it does not replace Kubernetes. Kubernetes remains responsible for creating and running pods, while Run:ai tracks organizational policy, quotas, GPU capacity and workload placement. A centralized control plane records the desired state, cluster components reconcile that state into Kubernetes resources, and the Run:ai Scheduler decides where AI workloads should run.
On GPU nodes, Run:ai can divide device memory into fractions, isolate each pod’s assigned memory and use NVIDIA time-slicing so several workloads share one GPU. Dynamic fractions let a workload borrow idle memory up to a configured limit, with reclamation protection when another workload needs the capacity.
What Run:ai adds to Kubernetes
Kubernetes supplies the general orchestration primitives: an API server, controllers, nodes, pods and a default scheduler. Run:ai extends those primitives for machine-learning and other GPU-heavy workloads. Its additional concepts include projects, departments, node pools, queues, deserved quota, fair-share, preemption, gang scheduling, topology-aware placement and fractional GPU memory.
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- Kubernetes creates, starts, stops and monitors pods and containers.
- Run:ai decides how AI workloads should be governed and placed, then expresses those decisions through Kubernetes resources and metadata.
- GPU-node components enforce memory boundaries, runtime policies and workload lifecycle rules on the cluster.
A request can enter through the Run:ai web interface, command-line interface, REST API or MCP server. The control plane authenticates the request and stores the desired state. Cluster synchronization brings that state into the target Kubernetes cluster, where admission and scheduling components prepare and place the workload. Status and usage then flow back to the control plane.
The three architectural areas
User interfaces and APIs
Users and automation submit projects, workloads, policies and resource requests through the web UI, CLI, REST API or MCP server. These entry points are ways to declare intent; they are not separate execution engines. The control plane remains the system that authenticates requests and records the resulting state.
Central control plane
The control plane stores organizational information, workload intent and status for the Run:ai environment. It provides the multi-cluster view used to manage policy and capacity across clusters, while the actual pods continue to run inside Kubernetes clusters.
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Each connected cluster runs an operator-managed set of controllers and services. They synchronize desired state, modify pod metadata when required, schedule workloads, enforce runtime rules and report status. The design is declarative: if a pod, queue or policy differs from the stored desired state, the relevant controller works to reconcile the difference.
Run:ai components in a Kubernetes cluster
The cluster plane is driven by Kubernetes custom resources and an operator. The named functional components have distinct responsibilities:
| Component | Primary responsibility |
|---|---|
| Data synchronization | Synchronizes state between the central control plane and the Kubernetes cluster. |
| Admission-controller | Modifies pod metadata and resource requirements before Kubernetes admits the pod. |
| Scheduler | Evaluates queues, quotas, priorities, topology and available resources, then selects placement. |
| Runtime-rules | Enforces configured idle-time and maximum-duration limits. |
| Anyworkload-controller | Translates workload custom resources into pods and manages their lifecycle. |
| Accessrule-controller | Applies access rules associated with Run:ai resources. |
| Shared-objects-controller | Manages shared objects used by Run:ai resources and synchronization. |
Current packaging also uses open-source building blocks. KAI Resource Management supplies abstractions for projects, departments, node pools, queues and workload placement. KAI Scheduler provides AI-aware scheduling. The GPU-fractioning operator enforces memory boundaries for shared allocations and exports per-pod metrics.
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Run:ai Scheduler versus the Kubernetes Scheduler
The Kubernetes Scheduler primarily matches a pod’s resource requests and constraints to a suitable node. Run:ai’s Scheduler reasons about those Kubernetes resources plus AI-specific organizational and sharing policies. Kubernetes still performs the final pod binding and runs the resulting workload; Run:ai supplies the higher-level placement decision and associated metadata.
| Decision area | Kubernetes Scheduler | Run:ai Scheduler |
|---|---|---|
| Basic placement | Matches pod requests and constraints to nodes. | Adds queue, project, department, node-pool and AI workload policy to placement. |
| Quota semantics | Uses Kubernetes resource accounting and configured scheduling constraints. | Honors deserved quota, then distributes unused capacity as over-quota resources according to queue rank, weight and fair-share. |
| Fairness | Does not provide Run:ai’s project- and department-level fair-share model. | Calculates fair-share per project or department and node pool, including historical GPU-hour usage when configured. |
| Preemption | Supports Kubernetes scheduler preemption behavior. | Applies Run:ai priorities and preemption policy, including consolidation of preemptible workloads. |
| Gang scheduling | General pod scheduling does not by itself provide Run:ai’s all-or-nothing group binding model. | Can keep every pod in a workload group pending until the group can be bound together. |
| GPU sharing | Schedules declared resources but does not provide Run:ai fractional-memory governance. | Works with GPU fractioning, dynamic borrowing, memory limits and per-pod GPU metrics. |
How quotas, fair-share and preemption interact
1. Deserved quota is considered first
Each project or department can have a deserved share of resources within a node pool. When capacity is available, the Scheduler first tries to satisfy workloads that are within that entitlement. This prevents a busy queue from immediately consuming the capacity reserved for another organizational group.
2. Unused capacity becomes over-quota capacity
If a project has no pending demand, its unused quota can be lent to other queues. A workload may therefore run above its deserved share without permanently changing the underlying entitlement. Queue rank, configured weight and fair-share influence which projects receive this borrowed capacity.
3. Fair-share limits repeated overuse
Fair-share is a numerical value calculated for a project or department within a node pool. It combines deserved resources with a share of currently unused resources. Where time-based accounting is enabled, historical GPU-hour consumption can affect the value. A group that has consumed more of the available capacity over time can receive a lower effective priority than a group that has used less, even when both have pending work.
4. Priority and preemption resolve contention
When demand exceeds capacity, priorities determine which pending workloads are more important. Preemption allows eligible lower-priority or preemptible workloads to be stopped so that a higher-priority workload can run. If resources are scattered across nodes or devices, the Scheduler can consolidate by rescheduling preemptible workloads and freeing a contiguous region.
5. Gang scheduling prevents partial launches
Distributed training often needs several pods at once. With gang scheduling, the group is bound together or remains pending. This avoids starting only part of a training job, consuming resources while the remaining workers wait indefinitely. The trade-off is that a large group can remain pending until all required GPUs and placement constraints are simultaneously available.
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Node pools, projects and departments
These terms describe different dimensions of the scheduling model:
- Node pool: a logical group of hardware. It might represent a GPU generation, an availability zone or another operational boundary.
- Project: a queueing and governance unit for workloads, commonly aligned with a team or application.
- Department: a higher-level organizational grouping that can contain or govern projects.
Scheduling decisions are scoped per node pool. Two hardware groups can therefore have independent queues, quota accounting and fair-share behavior. A project may be entitled to capacity in one node pool without receiving the same entitlement in another.
How Run:ai shares one GPU between pods
Memory fractions
Run:ai’s fractioning logic divides GPU memory into dynamic chunks. A pod requests a memory amount or fraction, and the fractioning operator gives it a separate virtual memory address space. The intended boundary is that a workload cannot consume more than its assigned memory or overwrite another pod’s allocated memory.
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Exact memory versus percentage requests
An exact memory request is preferable when a workload needs a known capacity. Percentage requests depend on the installed GPU’s memory size, so the same percentage represents different numbers of gigabytes on different hardware.
Multi-GPU fractions
A multi-GPU fraction applies the same request across several devices on one node. For example, a workload can request 8 × 40 GB on eight 80 GB GPUs. It receives 40 GB on each device, leaving 40 GB per GPU for other workloads.
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| Example allocation | Per-device result | Remaining capacity |
|---|---|---|
| 8 × 40 GB on eight 80 GB GPUs | 40 GB assigned on each of eight GPUs | 40 GB remains on each GPU |
Dynamic fractions: request and limit
Dynamic fractions add a guaranteed request and an upper limit. Consider a workload requesting 0.25 GPU with a limit of 0.80 GPU. The 0.25 allocation is guaranteed. When memory is free, the workload may borrow additional capacity up to 0.80. If another workload needs that memory, the borrowed portion can be reclaimed.
GPUOOMKiller supplies quality-of-service protection during reclamation. It helps identify and terminate or protect workloads according to the configured policy when memory must be recovered, rather than allowing an uncontrolled overrun to affect every pod on the device.
Fragmentation and MIG limitations
Fractional allocations can fragment free memory. Bin-packing can reduce this effect by concentrating workloads and preserving larger unused regions. Run:ai GPU fractions cannot be used on the same node as NVIDIA MIG mode, so a node design must choose one sharing mechanism rather than combining both.
What runs on each GPU node
The node does not run the entire Run:ai control plane. It runs the Kubernetes node services plus the cluster components that enforce the centrally managed policy. In practice, the GPU node participates in these functions:
- The admission-controller prepares incoming pods with Run:ai scheduling metadata and resource requirements.
- The Scheduler evaluates the node as part of its node-pool and queue calculations.
- The GPU-fractioning operator applies per-pod memory boundaries and emits GPU usage metrics.
- Runtime-rules can stop workloads that exceed configured idle or duration limits.
- The anyworkload-controller creates and manages pods represented by workload custom resources.
- Synchronization and shared-object controllers keep cluster resources aligned with the control plane.
Kubernetes then performs the ordinary node work: it starts containers, mounts volumes, manages networking and reports pod health. Run:ai governs the AI-specific layer without replacing those Kubernetes responsibilities.
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- A user submits a distributed training workload through the CLI, web UI, REST API or MCP server and selects a project, node pool and GPU request.
- The control plane authenticates the request and records the desired workload state.
- Cluster synchronization delivers the relevant custom resource or policy to the target Kubernetes cluster.
- The anyworkload-controller translates the workload object into pods. The admission-controller adds Run:ai metadata and adjusts resource requirements.
- The Scheduler checks the project’s deserved quota, available over-quota capacity, fair-share value, priority, topology and gang requirements.
- If enough compatible GPUs are available, the group is bound together. If not, it remains pending; eligible preemptible workloads may be moved to create capacity.
- On each selected GPU, fractioning enforces the requested memory boundary and time-slicing shares compute access.
- Runtime rules and GPU metrics are applied while the pods run. Status and usage are sent back to the control plane.
- If a dynamic-fraction borrower exceeds its guaranteed share while another workload needs memory, the borrowed allocation can be reclaimed under GPUOOMKiller protection.
Placement strategies and topology
Bin-packing
Bin-packing concentrates workloads on fewer devices or nodes. The goal is to preserve larger contiguous free regions elsewhere, which is useful when later jobs need large GPU or multi-GPU allocations.
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Spreading
Spreading distributes workloads across nodes or devices. It can reduce concentration and may improve resilience or throughput when a workload benefits from broader placement.
Topology-aware placement
The Scheduler can account for topology when selecting nodes and devices. This matters for multi-GPU jobs whose performance depends on the relationship between devices, nodes or other hardware boundaries. Topology is evaluated together with quota, fair-share and gang requirements rather than as an isolated filter.
Deployment and adoption choices
Run:ai’s current packaging includes open-source components such as KAI Resource Management, KAI Scheduler and the GPU-sharing operator. Organizations can adopt those components as part of an open-source-oriented platform, or use supported commercial paths:
- NVIDIA AI Enterprise: an enterprise software route that includes Run:ai capabilities within NVIDIA’s supported AI platform offering.
- Standalone Run:ai: a separately supported Run:ai deployment for organizations that want the platform’s scheduling and governance layer.
- Certified partners: providers in NVIDIA’s partner network can supply deployment or support services.
The appropriate choice depends on whether the organization needs a supported enterprise subscription, a self-managed open-source stack, or assistance from a certified provider. The architecture remains the same at its core: a control plane manages desired state, cluster components reconcile it, and Kubernetes runs the pods.
Troubleshooting common scheduling symptoms
A pod stays pending even though GPUs appear free
Check whether the free memory is fragmented, whether the project has exhausted deserved quota, and whether the request is waiting for over-quota access. For a gang-scheduled workload, individual free GPUs are insufficient if the full group cannot be placed together. Node-pool selection and topology constraints can also exclude otherwise available devices.
A workload runs below its expected GPU share
Verify the distinction between memory and compute. A fraction controls memory allocation; compute is shared through time-slicing and is not automatically proportional to the requested memory fraction. Queue priority, fair-share and preemption can also affect when a workload receives capacity.
A dynamic-fraction workload loses borrowed memory
This is expected when another workload needs the capacity. The guaranteed request remains the baseline, while memory above that request is reclaimable up to the configured limit. Review the request, limit and GPUOOMKiller policy if the workload requires a hard memory guarantee.
Placement changes after another job arrives
Run:ai can preempt eligible workloads or consolidate preemptible jobs to satisfy higher-priority demand and reduce fragmentation. Confirm workload priority and preemptibility settings before treating a reschedule as a node failure.
Fractioning cannot be enabled on a node
Check whether the node is configured for MIG. Run:ai GPU fractions and MIG are mutually exclusive on the same node, so the hardware pool must be redesigned or assigned to one mechanism.
Quick Recap
Architecture checklist
- Keep Kubernetes as the pod execution and lifecycle layer.
- Use the Run:ai control plane for organizational state, policy and multi-cluster visibility.
- Define node pools before assigning quota, because fairness and accounting are scoped to them.
- Set deserved quota for baseline entitlement and use fair-share and queue weights to govern borrowed capacity.
- Use priorities, preemption and gang scheduling deliberately for mixed interactive and distributed-training workloads.
- Choose bin-packing when preserving large free regions matters; choose spreading when distribution is preferable.
- Request exact GPU memory when hardware-independent capacity is important.
- Use dynamic fractions only when the workload can tolerate reclaimable memory above its guarantee.
- Do not combine Run:ai fractions with MIG on the same node.
- Monitor per-pod GPU metrics and runtime-rule events so policy effects are visible to operators.
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