Freedom251 › GPU Cluster Management Software

KAI Scheduler

Free plan Self-hosted

What KAI Scheduler is

KAI Scheduler is a self-hosted, open-source Kubernetes scheduler for assigning GPUs to AI workloads such as data processing, training, and inference. It supports work ranging from interactive notebooks to multi-node distributed training in one cluster, and is designed for large GPU clusters and high-throughput workloads. Teams can arrange jobs in hierarchical queues with quotas, limits, priorities, and resource borrowing across teams. Gang scheduling holds a distributed workload until all its pods can be placed together. Elastic workloads can grow or shrink within configured minimum and maximum replica counts as capacity changes. GPU sharing supports time slicing, MPS, and MIG. Other scheduling controls include topology-aware placement, time-based fairshare based on historical GPU use, and priority-based preemption. KAI documents native integration for Ray workloads on Kubernetes and integration with Grove for complex workloads. It supports cloud and on-premises deployments, including cloud autoscalers such as Karpenter. Installation choices include a production Helm chart, building from source, or ArgoCD. A running Kubernetes cluster and Helm are required; workloads requesting GPU resources also require NVIDIA GPU Operator. The project is Apache-2.0 licensed and a CNCF sandbox project. LTS releases receive one year of public support, including security patches and critical bug fixes.

Who it suits

KAI Scheduler suits teams running GPU-based AI workloads on Kubernetes that need queue controls, GPU sharing, or coordinated distributed scheduling. It can serve both cloud and on-premises infrastructure, but teams need to operate a Kubernetes cluster and Helm.

What it does well

Open-source Apache-2.0 project with no listed price.
Supports training, inference, and data-processing workloads.
Hierarchical queues provide quotas, limits, priorities, and borrowing.
GPU sharing supports time slicing, MPS, and MIG.
Runs on cloud and on-premises infrastructure.
LTS releases receive one year of public support.

What to know first

Requires a running Kubernetes cluster and Helm CLI.
GPU-requesting workloads require NVIDIA GPU Operator.
Self-hosted deployment requires infrastructure management.
Default NonMemoryEnforced mode does not enforce runtime GPU memory isolation.

Getting started

Prepare a running Kubernetes cluster and install the Helm CLI.
Install NVIDIA GPU Operator if workloads request GPU resources.
Install KAI Scheduler using its production Helm chart, ArgoCD, or a source build.
Open GitHub issues for bugs, feature suggestions, or technical help.

Verdict

Choose KAI Scheduler if you need to allocate GPUs across Kubernetes AI workloads with queue policies, gang scheduling, and GPU sharing. Look elsewhere if you need a managed service or enforced runtime GPU memory isolation in the default NonMemoryEnforced mode.

Questions

How much does KAI Scheduler cost?

It is an open-source Apache-2.0 project; no price is listed.

Is KAI Scheduler self-hosted?

Yes. The deployment model is self-hosted, with support for cloud and on-premises deployments.

What is required to install it?

The installation guide requires a running Kubernetes cluster and Helm CLI. NVIDIA GPU Operator is also required for workloads requesting GPU resources.

What integrations are documented?

KAI documents native integration for Ray workloads on Kubernetes and integration with Grove for complex workloads.

What license does it use?

The public repository lists an Apache-2.0 license, and the project is identified as a CNCF sandbox project.

What support is available?

LTS releases receive one year of public support, including security patches and critical bug fixes. The project directs users to GitHub issues for bugs, suggestions, or technical help.

Facts from KAI Scheduler's own pages

Cloud and on-premises: The project describes compatibility with dynamic cloud infrastructure, including Karpenter autoscalers, and static on-premises deployments. · github.com, 7 Oct 2026
Deployment: The project provides production Helm installation, source build, and ArgoCD GitOps installation options for Kubernetes. · github.com, 7 Oct 2026
Elastic workloads: Elastic workloads can grow or shrink within configured minimum and maximum replica counts as capacity changes. · kai-scheduler.dev, 5 Oct 2026
Fairness: Time-based fairshare tracks historical GPU usage over a configurable window to distribute over-quota resources fairly over time. · kai-scheduler.dev, 7 Oct 2026
Gang and elastic scheduling: Gang scheduling places distributed workloads all at once, while elastic workloads can grow or shrink within minimum and maximum replica counts as capacity allows. · kai-scheduler.dev, 7 Oct 2026
Gang scheduling: Gang scheduling places all pods in a distributed workload together or waits until resources are available. · github.com, 5 Oct 2026
GPU sharing: GPU sharing supports time slicing, MPS, and MIG so workloads can share GPU devices. · kai-scheduler.dev, 7 Oct 2026
GPU sharing limitation: The GPU-sharing documentation says the default NonMemoryEnforced mode schedules fractional GPU workloads without KAI enforcing runtime GPU memory isolation. · github.com, 7 Oct 2026
Hierarchical queues: Multi-level queue trees support quotas, limits, and over-quota borrowing across teams. · kai-scheduler.dev, 7 Oct 2026
Integrations: The project states it is natively integrated with KubeRay for Ray workloads on Kubernetes and describes integrations with Grove and Dynamo for complex multi-component workloads. · github.com, 7 Oct 2026
Intended workloads: It supports workloads ranging from interactive notebooks to multi-node distributed training in the same cluster. · kai-scheduler.dev, 7 Oct 2026
License: The public GitHub repository identifies the project as open source and lists an Apache-2.0 license. · github.com, 7 Oct 2026
License and project: KAI Scheduler is an open-source Apache-2.0 project and a CNCF sandbox project. · github.com, 5 Oct 2026
Prerequisite: The installation guide says NVIDIA GPU-Operator is required to schedule workloads requesting GPU resources. · github.com, 5 Oct 2026
Prerequisites: The installation guide lists a running Kubernetes cluster and Helm CLI, and requires NVIDIA GPU Operator for workloads requesting GPU resources. · github.com, 7 Oct 2026
Priority and preemption: Per-queue and per-workload priorities let critical jobs preempt while non-critical jobs back off. · kai-scheduler.dev, 7 Oct 2026
Purpose: KAI Scheduler is a Kubernetes-native scheduler for AI workloads that optimizes GPU allocation across data processing, training, and inference while keeping resource shares fair across teams. · kai-scheduler.dev, 7 Oct 2026
Queues: Hierarchical queues support quotas, limits, priorities, and borrowing resources across teams. · kai-scheduler.dev, 5 Oct 2026
Scale: The project says it is designed and continuously tested for large GPU clusters with thousands of nodes and high-throughput workloads. · kai-scheduler.dev, 5 Oct 2026
Security: Security vulnerabilities should be reported through GitHub Security Advisories rather than public GitHub issues. · github.com, 5 Oct 2026
Support: The project directs users to GitHub issues for bugs, feature requests, or technical help, and also lists CNCF Slack, community calls, and a mailing list. · github.com, 7 Oct 2026
Support channel: The project directs users to open GitHub issues for bugs, feature suggestions, or technical help. · github.com, 5 Oct 2026
Supported infrastructure: The project states it supports cloud and on-premise deployments, including cloud auto-scalers such as Karpenter. · github.com, 5 Oct 2026
Topology-aware placement: KAI supports topology-aware scheduling, including hierarchical topology-aware scheduling for hierarchical PodGroups. · kai-scheduler.dev, 7 Oct 2026

Where KAI Scheduler stands on Freedom251

GPU Cluster Management Software#14 of 21