kaito-project/kaito

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

1009 stars · 183 forks observed · 2026-09-03

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

KAITO is a Kubernetes operator suite that automates LLM inference, fine-tuning, and RAG engine deployment using simplified CRD APIs. It auto-provisions GPU nodes with memory-based estimation and supports any vLLM-compatible HuggingFace model.

Use cases

• deploy llm inference on kubernetes
• fine-tune llm models in a cluster
• run vllm models on gpu nodes
• auto-provision gpu nodes for model serving
• deploy rag engine in kubernetes
• distributed multi-node llm inference
• simplify llm deployment with crd

When to choose

• you run Kubernetes and want declarative LLM inference or fine-tuning workloads
• you want automatic GPU node provisioning with memory estimation
• you need preset-optimized parallelism settings instead of hand-tuning vLLM parameters
• you want to serve any vLLM-supported HuggingFace model

When to avoid

• you don't use Kubernetes
• you need a lightweight single-machine inference server
• you need fine-grained control over every inference engine parameter
• your cluster cannot provision GPU nodes

Facets

service · maturity active

llm-inference llm-training rag container-orchestration deployment gpu-computing large-language-models machine-learning cloud-computing infrastructure-as-code go cloud self-hosted kubernetes-operator vllm fine-tuning gpu-provisioning crd distributed-inference karpenter kubernetes docker

Member repositories

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markdown · JSON · MCP: product_card(name="kaito-project/kaito")