1.vllm-project / vllm
A high-throughput and memory-efficient inference and serving engine for LLMs
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35 repositories by Attention Score · Oct 27, 2026 – Nov 2, 2026 (UTC)
A high-throughput and memory-efficient inference and serving engine for LLMs
Local inference for diffusion language models (LLaDA, Dream), written in Mojo
Make videos on the computer you already own. FreeVideo runs MiniMax H3 in as little as 8 GB of VRAM and 16 GB of RAM, and adapts its acceleration path to your hardware.
Faster Whisper transcription with CTranslate2
SGLang is a high-performance serving framework for large language models and multimodal models.
Port of OpenAI's Whisper model in C/C++
Remory: Learning Residual Memory for Context Compaction
🧱 Experimental library for building inference engines
Nano vLLM
Cross-platform, customizable ML solutions for live and streaming media.
LMCache: Supercharge Your LLM with the Fastest KV Cache Layer
Berth is a lightweight GPU resource manager written in Go. It controls which applications get access to your GPU, when they get it, and how that access is handed off.
Open decision models from 0.8B to 27B: a calibrated probability for every option in one pass, and a short thought only when unsure. Engine for vLLM, llama.cpp and MLX.
Infrastructure for continually self‑improving agents
Machine Learning Engineering Open Book
Don't memorize ML. Understand it from first principles including Inference, Cuda, Deep Learning, Data Science and Reinforcement Learning
Manages Unified Access to Generative AI Services built on Envoy Gateway
OpenVINO™ is an open source toolkit for optimizing and deploying AI inference
Ultralytics YOLOv5 in PyTorch for object detection, instance segmentation, classification, training, and export.
ncnn is a high-performance neural network inference framework optimized for the mobile platform
llama.cpp fork that abliterates at inference
NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
Feel how fast a local LLM runs before you buy the hardware
The Triton Inference Server provides an optimized cloud and edge inferencing solution.
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
🎨 The exhaustive Pattern Matching library for TypeScript, with smart type inference.
PyTorch implementation of YOLOv3, YOLOv3-SPP, and YOLOv3-tiny for real-time object detection with training, validation, inference, and multi-format export.
Open-source library of optimized deep learning operations (matmul, convolution, attention) for CPUs (x64, AArch64, RISC-V) and Intel GPUs. Used by PyTorch, TensorFlow, OpenVINO, and ONNX Runtime.
Kubernetes operator for self-hosted LLM inference across a heterogeneous GPU fleet: NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal. Runtimes: llama.cpp, vLLM, TGI, mlx-server. Multi-GPU sharding, model caching, OpenAI-compatible endpoints. Apache-2.0, run across homelab and on-prem fleets, actively developed.
Making large AI models cheaper, faster and more accessible
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
ModelSphere is an open-source LLM inference infrastructure designed to make production-grade model serving simple, efficient, and continuously optimized.
Production ready toolkit to run AI locally
Superduper: End-to-end framework for building custom AI applications and agents.
TypeQL: Built for systems, not records