Open source. Open possibilities.

SGLang is a high-performance serving framework for large language models (LLMs) and multimodal models, designed for low-latency and high-throughput inference.

LEANN is a lightweight vector database designed for personal RAG applications, reducing storage requirements by up to 97% through graph-based selective recomputation.

vLLM is a high-throughput, memory-efficient library designed for Large Language Model (LLM) inference and serving.

Oumi is an open-source platform for the full foundation-model lifecycle: data preparation, training and fine-tuning (SFT, LoRA, QLoRA, GRPO), evaluation, data synthesis with LLM judges, and deployment via vLLM/SGLang, with a CLI and cloud job launching.

Ollama is a tool that allows users to run, manage, and build with open-source large language models locally on macOS, Windows, and Linux.