data-infra/cube-studio

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

2448 stars · 196 forks observed · 2026-08-28

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

CubeStudio is an open-source, cloud-native, all-in-one AI platform covering the full machine learning lifecycle (MLOps/MaaS/LLMOps), including multi-tenant compute scheduling, drag-and-drop pipeline orchestration, distributed training, hyperparameter search, and LLM fine-tuning/inference. It runs on Kubernetes, supports vGPU virtualization, RDMA networking, edge clusters, and domestic heterogeneous accelerators (Ascend, Cambricon, Hygon, Moore Threads, MetaX) with private/offline deployment.

Use cases

• deploy a self-hosted mlops platform on kubernetes
• orchestrate training pipelines with drag-and-drop
• run multi-node multi-gpu distributed training
• fine-tune llms like deepseek with sft or rlhf
• serve llm inference with vllm or ollama
• schedule and rent gpu/npu compute across teams
• build a private knowledge base rag chatbot
• automate multimodal data annotation

When to choose

• you need a full-chain AI platform covering data, training, and inference in one self-hosted deployment
• your organization uses domestic heterogeneous accelerators (Ascend, Cambricon, Hygon) or requires xinchuang/offline deployment
• you need multi-tenant compute scheduling, quota billing, and compute rental
• you want drag-and-drop pipeline orchestration plus distributed training and hyperparameter search
• you need LLM fine-tuning, multi-node inference, and private RAG agents in one platform

When to avoid

• you only need a lightweight experiment tracking or model registry tool rather than a full platform
• you cannot operate a Kubernetes cluster or lack ops capacity for platform maintenance
• you need a managed cloud service with vendor support instead of self-hosted software
• your workload is a single small model with no need for multi-tenancy or scheduling

Facets

application · maturity active

machine-learning deep-learning llm-inference llm-training rag agent-framework workflow-automation scheduling monitoring data-visualization etl container-orchestration web-framework self-hosted machine-learning deep-learning large-language-models artificial-intelligence gpu-computing self-hosted cloud-computing microservices self-hosted python cross-platform mlops maas llmops ai-platform vgpu rdma pipeline-orchestration distributed-training hyperparameter-search inference-serving multi-tenant domestic-accelerators ascend-npu edge-computing compute-scheduling model-marketplace annotation-platform fine-tuning reinforcement-learning xinchuang data-engineering containers kubernetes docker web-server linux gpu

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markdown · JSON · MCP: product_card(name="data-infra/cube-studio")