kai-scheduler/KAI-Scheduler resource

Health v2 · maintenance only

Adoption not part of the score

1472 stars · 253 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

KAI Scheduler is a Kubernetes-native scheduler written in Go that optimizes GPU resource allocation for AI and machine learning workloads at large scale. It supports the full AI lifecycle from small interactive jobs to large training and inference workloads, with fairness, gang scheduling, and topology-aware placement across clusters of thousands of nodes.

Use cases

• schedule GPU workloads on a large Kubernetes cluster
• run training and inference jobs fairly across teams
• gang scheduling for distributed AI training jobs
• topology-aware placement for disaggregated LLM serving
• maximize GPU utilization in a shared AI cluster
• dynamically allocate GPUs to pods in Kubernetes

When to choose

• you operate a large multi-tenant GPU cluster on Kubernetes
• you need fair-share GPU allocation between teams
• you run both interactive notebooks and large distributed training in one cluster
• you need topology-aware or gang scheduling for AI workloads

When to avoid

• you have a small cluster with no GPU workloads
• the default kube-scheduler already meets your needs
• you need scheduling for non-Kubernetes environments

Facets

infra-config · maturity active

container-orchestration scheduling gpu-computing deployment infrastructure-as-code machine-learning cloud-computing gpu-computing go cloud self-hosted kubernetes-scheduler gpu-scheduling gang-scheduling ai-workloads fair-share topology-aware-scheduling training-jobs inference containers kubernetes linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="kai-scheduler/KAI-Scheduler")