Awesome GitHub RepositoriesMulti-Node

mistral.rs is an inference engine for large language models that runs locally and exposes models behind OpenAI and Anthropic-compatible APIs. It serves as a multi-model serving platform, capable of loading several models in a single server process with per-request routing and on-demand loading and unloading. The engine supports multimodal inference, processing text alongside images, video, audio, and speech inputs, and includes a quantized model deployment runtime that reduces memory use and speeds up inference on consumer hardware. The project distinguishes itself through an agentic tool exe

Dynamo is a distributed inference orchestration platform designed for large language models. It functions as a system to coordinate prefill and decode phases across GPU nodes, utilizing a multi-backend runtime adapter to connect engines like vLLM and TensorRT-LLM through a unified block-oriented memory interface. An OpenAI-compatible API server provides the frontend for integration with existing tools and clients. The project is distinguished by its disaggregated serving architecture, which separates prompt processing and token generation onto independent GPU pools to optimize throughput and

FlashInfer is a library of high-performance GPU kernels purpose-built for accelerating large language model inference. It provides optimized implementations for attention operations (including flash attention, page attention, multi-head latent attention, and cascade attention) using paged key-value caches, fused kernel composition, and just-in-time compilation. The library also includes specialized kernels for mixture-of-experts layers, block-scaled low-precision quantization (FP8, FP4), and distributed collective communication. What distinguishes FlashInfer is its fused all-reduce communicat

NCCL es una biblioteca de comunicación de alto rendimiento y un framework de computación distribuida en GPU diseñado para ejecutar intercambios de datos colectivos y punto a punto a través de múltiples GPUs en sistemas de uno o varios nodos. Sirve como capa de transporte RDMA para GPU y orquestador de memoria, facilitando la sincronización de gran ancho de banda de datos y gradientes de modelos para el entrenamiento e inferencia distribuida en GPU. La biblioteca se distingue por su capacidad para ejecutar primitivas de comunicación directamente desde kernels de GPU, eliminando la CPU anfitriona del camino crítico. Utiliza la selección de rutas consciente de la topología para optimizar el movimiento de datos y emplea transporte de red basado en RDMA, incluyendo InfiniBand y NVLink, para permitir el acceso a memoria de copia cero entre dispositivos a través de diferentes nodos físicos. El proyecto cubre una amplia gama de patrones de comunicación colectiva, incluyendo reducciones, broadcasts, gathers e intercambios all-to-all, junto con acceso remoto a memoria punto a punto. Proporciona una gestión integral de comunicadores para inicializar, particionar y redimensionar grupos de GPU, así como una gestión de memoria especializada para registrar buffers y coordinar memoria compartida de dispositivo. El sistema incluye un conjunto de herramientas de monitoreo y observabilidad para el seguimiento de la salud, registro de diagnósticos y monitoreo de eventos en tiempo real, así como interfaces de integración para frameworks de aprendizaje automático, CUDA graphs, MPI y Python.

Distributed-llama is a distributed inference engine and command line tool for running large language models across multiple networked machines. It functions as a compute cluster manager that coordinates worker nodes to share the computational load of a single model. The system utilizes tensor parallelism to shard model weights across different hosts, allowing the execution of models that exceed the memory capacity of a single piece of hardware. It includes a dedicated format converter to transform standard model files into a compatible binary layout optimized for distributed loading. The eng