LobeChat

Self-hosted AI platform with ChatGPT-like interface for local and cloud LLMs.

Extensible, self-hosted AI platform with 290M+ Docker pulls and 124K+ GitHub stars. Supports Ollama, OpenAI-compatible APIs, and any Chat Completions backend. Features built-in RAG, multi-user RBAC, voice/video calls, Python function workspace, model builder, and web browsing. Runs entirely offline with enterprise features including SSO and audit logging.

All-in-one self-hosted AI app with RAG, agents, and multi-user support

AnythingLLM is an open-source, privacy-first AI application that turns any document into an interactive knowledge base. It bundles document ingestion, vector storage (built-in LanceDB), RAG pipelines, AI agents, and multi-user access into a single deployable package. Supports 30+ LLM providers including OpenAI, Anthropic, Ollama, and local models. With 62K+ GitHub stars and MIT license, it runs as a desktop app or Docker container with zero configuration required out of the box.

Offline-first AI assistant for local inference

Jan is an open-source offline-first AI assistant with 25K+ GitHub stars running LLMs locally without sending data externally. Features a ChatGPT-like interface with one-click model downloads from Hugging Face, conversation management, customizable prompts, and an OpenAI-compatible local API server. Supports GGUF models via llama.cpp with GPU acceleration on NVIDIA and Apple Silicon. Built with Electron for macOS, Windows, and Linux with full data privacy.

Run LLMs locally with one command

Tool for running large language models locally on your machine with a simple CLI interface. Download and run Llama 3, Mistral, Gemma, Phi, Code Llama, and dozens of other open-source models with a single command. Features model management, GPU acceleration (NVIDIA/AMD/Apple Silicon), OpenAI-compatible API server, Modelfile for customization, and multi-model switching. Ideal for offline AI development, privacy-sensitive use cases, and local testing. 120K+ GitHub stars.

Heterogeneous CPU-GPU inference and SFT for large MoE models

Open-source framework for running and fine-tuning large Mixture-of-Experts models with heterogeneous CPU-GPU execution, optimized kernels, limited VRAM and SGLang or LLaMA-Factory integrations.

Official Kubernetes and Helm reference stack built on the vLLM inference engine

Official vLLM reference implementation for scaling the existing inference engine on Kubernetes with Helm, request routing, KV-cache offload, autoscaling and Prometheus/Grafana observability.

Distributed inference orchestration above vLLM, SGLang and TensorRT-LLM

Open-source, datacenter-scale orchestration layer that coordinates vLLM, SGLang and TensorRT-LLM across nodes with disaggregated serving, KV-aware routing, multi-tier cache management and automatic scaling.

Open-source GPU control plane for scalable AI model serving

Open-source GPU cluster manager that configures vLLM, SGLang, TensorRT-LLM or custom engines, serves models through compatible APIs, and provisions SSH-accessible GPU instances across on-premises, Kubernetes and cloud environments.

Disaggregated KV cache storage and transfer for LLM serving

Open-source infrastructure for disaggregated LLM serving that pools KV caches across prefill and decode workers, with high-performance transfer, distributed storage and integrations for vLLM and SGLang.

Reusable KV cache infrastructure for scalable LLM inference

Open-source KV cache management layer that persists, offloads and reuses model key-value caches across requests and serving engines to reduce repeated prefill work and improve inference throughput.