vLLM is a high-throughput, memory-efficient inference and serving engine for large language models (LLMs). It implements PagedAttention for near-zero waste KV cache management, continuous batching, chunked prefill, prefix caching, CUDA/HIP graphs, speculative decoding, and torch.compile. The system serves OpenAI-compatible APIs (chat completions, completions, embeddings, rerank, speech-to-text), Anthropic Messages API, Cohere API, and custom pooling/classification endpoints. It supports 200+ model architectures including dense, MoE, hybrid SSM/attention, and multimodal models (vision, audio, video). The architecture is a hybrid Python/Rust system: a Python core handles model execution, scheduling, and kernel dispatch, while a Rust frontend provides a high-performance HTTP/gRPC server with middleware for auth, metrics, and load management. Key entry points include the `vllm` CLI (serve, bench, launch), the `LLM` class for offline inference, and the OpenAI-compatible API server. External dependencies include PyTorch, HuggingFace Transformers, FlashAttention, CUTLASS, Triton, NCCL/RCCL, and various quantization libraries. The project is production-grade with extensive CI/CD, Docker images, Helm charts, and deployment integrations for Kubernetes, Ray, SageMaker, and more.

This repository implements a production-grade semantic router for LLM inference, functioning as an Envoy ext_proc filter that intercepts chat completion requests, classifies them via multiple signals (intent, PII, jailbreak, domain, embeddings, etc.), and routes to the most appropriate model or model composition. The system supports Mixture-of-Models (MoM) with configurable algorithms (fusion, cascading, workflows) and provides advanced features like semantic caching, context compression, rate limiting, and observability. It includes a full-stack dashboard (React + Go backend) for configuration management, evaluation/experimentation (campaigns, A/B tests, capacity SLOs), and monitoring. A Python CLI and Kubernetes operator are available for deployment. The codebase demonstrates high engineering quality with extensive validation, testing, and a modular architecture, though its polyglot nature (Go, Rust, Python, TypeScript) and numerous optional backends introduce maintenance complexity. While research elements (training scripts, benchmarks) exist, the primary character is a deployable application.

AIBrix is a Kubernetes-native control plane and intelligent gateway for serving production LLM workloads. The repository implements two intertwined planes: a data plane composed of Envoy ext_proc gateway plugins (cmd/plugins, pkg/plugins/gateway) that intercept OpenAI-compatible inference requests and route them to vLLM, SGLang, or TensorRT-LLM replicas using algorithms such as least-load, prefix-cache-aware routing, SLO queueing, token-load tracking, and prefill/decode disaggregation; and a control plane of Kubernetes controllers (cmd/controllers) that reconcile custom resources for autoscaling (PodAutoscaler), LoRA adapters, model warmup/claims, KV caches, RayCluster fleets, and StormService/RoleSet orchestration. Supporting runtime code includes a Python metadata and batch-job service, a distributed KV-cache package, a GPU optimizer, and an OpenAI-compatible frontend.

compressed-tensors is a production-grade Python library for compressing and quantizing large neural network models, primarily targeting LLMs. It extends the safetensors format to store and manage compressed tensors, supporting a wide range of quantization schemes (INT4/INT8/FP8/NVFP4/MXFP4/MXFP8), sparsity patterns, and rotation-based transforms (Hadamard, random matrix). The library is tightly integrated with the vLLM inference engine and Hugging Face transformers, providing a seamless path from model compression to deployment. Its architecture is modular: a registry pattern for compressors and transforms, a quantization lifecycle with calibration and forward hooks, a flexible offloading subsystem (CPU/disk/distributed) for running models beyond VRAM limits, and checkpoint conversion pipelines for formats like AutoAWQ and ModelOpt NVFP4. While primarily a software library, it also serves as a research platform for novel quantization techniques, with benchmarks and experimental features like MoE expert pruning and KV cache quantization. The codebase is well-structured with extensive tests, CI/CD pipelines, and active maintenance, though it exhibits some coupling to vLLM internals and experimental components that may not be stable.