A unified inference and post-training framework for accelerated video generation.
Pre-trained Deep Learning models and demos (high quality and extremely fast)
LightLLM is a Python-based LLM (Large Language Model) inference and serving framework, notable for its lightweight design, easy scalability, and high-speed performance.
Optimizing inference proxy for LLMs
A curated resource list for learning AI performance engineering, from GPU fundamentals to production inference.
Rapid-MLX is an open-source (Apache 2.0) OpenAI- and Anthropic-compatible LLM inference server for Apple Silicon, built on MLX, focused on reliable tool calling for coding agents. Up to 4× faster than Apple's MLX (mlx-lm) on the same weights.
A minimal Python framework for building custom AI inference servers with full control over logic, batching, and scaling.
State-of-the-art LLM compression, built for production inference with vLLM
High-performance Inference and Deployment Toolkit for LLMs and VLMs based on PaddlePaddle
A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit and 4-bit floating point (FP8 and FP4) precision on Hopper, Ada and Blackwell GPUs, to provide better performance with lower memory utilization in both training and inference.
🚀 Accelerate inference and training of 🤗 Transformers, Diffusers, TIMM and Sentence Transformers with easy to use hardware optimization tools
Open-source inference server and production cluster for all the models your agent needs.
Python Toolkit for Causal and Probabilistic Reasoning
RamaLama is an open-source developer tool that simplifies the local serving of AI models from any source and facilitates their use for inference in production, all through the familiar language of containers.
PyTorch native quantization for training and inference
Community maintained hardware plugin for vLLM on Huawei Ascend
High-performance inference framework for large language models, focusing on efficiency, flexibility, and availability.
J-Space Cognition Suite — a model-agnostic inference-time control suite for deep reasoning, long-horizon work, verification, and recovery. Based on Anthropic's J-space global workspace research.
Lightweight Image Video Action Generation Inference Framework
Vietnamese TTS with instant voice cloning • On-device • Real-time CPU inference • 48kHz audio quality • Chuyển văn bản thành giọng nói tiếng Việt • Text to speech tiếng Việt • TTS tiếng Việt
The code used to train and run inference with the ColVision models, e.g. ColPali, ColQwen2, and ColSmol.
Images to inference with no labeling (use foundation models to train supervised models).
Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.
xDiT: A Scalable Inference Engine for Diffusion Transformers (DiTs) with Massive Parallelism
TokenSpeed is a speed-of-light LLM inference engine.
The fastest way to run Qwen 3.8 Flash Next, Qwen 3.8 27B and Ternary Bonsai 2 27B on a Mac: 125 tok/s in OpenCode on an M5 Max, and a 27B model on 16 GB Macs. Native MTP speculative decoding on Apple Silicon, exact at any temperature. OpenAI and Anthropic compatible local server.
cubestudio开源云原生一站式机器学习/深度学习/大模型AI平台/MaaS/mlops/人工智能平台/训推平台,算法全链路流程,多租户,算力租赁平台,token中转,拖拉拽任务流pipeline编排,多机多卡分布式训练,超参搜索,推理服务,VGPU虚拟化,云边端协同,边缘计算,自动化标注平台,deepseek等大模型sft微调/奖励模型/强化学习训练,vllm/ollama/mindie大模型多机推理,私有知识库llmops智能体,AI模型市场,支持国产异构算力调度,昇腾/寒武纪/海光/摩尔/沐曦等,支持ib/roce/RDMA,信创支持
Turn any computer or edge device into a command center for your computer vision projects.
An agentic company research tool powered by LangGraph and Tavily that conducts deep diligence on companies using a multi-agent framework. It leverages OpenRouter on the backend for rapid inference with any leading model.
A unified orchestration layer for heterogeneous AI compute. It standardizes how to manage compute and run training and inference on GPU clouds, Kubernetes, VMs, or bare-metal clusters.
Communicate with an LLM provider using a single interface
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.
Serve large Qwen models fast on the GPUs you actually own. Qwen3.8-27B on a single 24 GB card with vLLM: 127 tok/s single-user (381 when the answer quotes the prompt), ~1,035 tok/s at 64 concurrent, 150k-262k context. vLLM patches, requant pipeline, benchmarks.
Code, labs, and resources for O'Reilly AI Systems Performance Engineering: GPU optimization, distributed training, inference scaling, and full-stack tuning.
Gaussian processes in TensorFlow
Open Source AI Accelerator Research Platform Standard / 开源推理研究平台
Virtualized Elastic KV Cache for Dynamic GPU Sharing and Beyond
Large-scale LLM inference engine
Tile-Based Runtime for Ultra-Low-Latency LLM Inference
An optimized quantization and inference library for running LLMs locally on modern consumer-class GPUs
Evaluate and Enhance Your LLM Deployments for Real-World Inference Needs
Add bisenetv2. My implementation of BiSeNet
High-performance OpenAI and Anthropic compatible LLM inference server for Apple Silicon. Native MLX, continuous batching, multimodal models, MCP tool calling, and Claude Code support.
SGLang-Omni is a high-performance serving framework for audio models (TTS, ASR) and unified multimodal models.
Official SGLang x Datawhale course on LLM inference: understand inference, build a mini-sglang from scratch, then read the real SGLang source and land your first PR. Available in English and Chinese.
PyTorch compiler that accelerates training and inference. Get built-in optimizations for performance, memory, parallelism, and easily write your own.
V100 / SM70-focused vLLM engineering fork for modern LLM inference.
RTP-LLM: Alibaba's high-performance LLM inference engine for diverse applications.
Build, train, optimize, and run computer vision models locally, from raw images to live inference. Open source, optimized for Intel XPU (CPU-only and CUDA also supported).
Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application
Offline inference engine for art, real-time voice conversations, LLM powered chatbots and automated workflows
BAyesian Model-Building Interface (Bambi) in Python.
A PyTorch-native inference engine with cache, parallelism, quantization and cpu offload for DiTs.
OpenTelemetry Instrumentation for AI Observability
LLM KV cache compression made easy
Qualcomm® AI Hub Models is our collection of state-of-the-art machine learning models optimized for performance (latency, memory etc.) and ready to deploy on Qualcomm® devices.
[NeurIPS'24 Spotlight, ICLR'25, ICML'25] To speed up Long-context LLMs' inference, approximate and dynamic sparse calculate the attention, which reduces inference latency by up to 10x for pre-filling on an A100 while maintaining accuracy.
The simplest way to serve AI/ML models in production
Neural Network Compression Framework for enhanced OpenVINO™ inference
A Python package for causal inference in quasi-experimental settings