llm-serving

A high-throughput and memory-efficient inference and serving engine for LLMs

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

本项目旨在分享大模型相关技术原理以及实战经验（大模型工程化、大模型应用落地）

SGLang is a fast serving framework for large language models and vision language models.

Run any open-source LLMs, such as DeepSeek and Llama, as OpenAI compatible API endpoint in the cloud.

SkyPilot: Run AI and batch jobs on any infra (Kubernetes or 16+ clouds). Get unified execution, cost savings, and high GPU availability via a simple interface.

The easiest way to serve AI apps and models - Build Model Inference APIs, Job queues, LLM apps, Multi-model pipelines, and more!

Superduper: End-to-end framework for building custom AI applications and agents.

Multi-LoRA inference server that scales to 1000s of fine-tuned LLMs

AICI: Prompts as (Wasm) Programs

MoBA: Mixture of Block Attention for Long-Context LLMs

RayLLM - LLMs on Ray (Archived). Read README for more info.

High-performance inference framework for large language models, focusing on efficiency, flexibility, and availability.

A highly optimized LLM inference acceleration engine for Llama and its variants.

A high-performance ML model serving framework, offers dynamic batching and CPU/GPU pipelines to fully exploit your compute machine

A throughput-oriented high-performance serving framework for LLMs

RTP-LLM: Alibaba's high-performance LLM inference engine for diverse applications.

LLM (Large Language Model) FineTuning

🧬 Helix is a private GenAI stack for building AI applications with declarative pipelines, knowledge (RAG), API bindings, and first-class testing.

Community maintained hardware plugin for vLLM on Ascend