1.vllm-project / vllm
A high-throughput and memory-efficient inference and serving engine for LLMs
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18 repositories by Attention Score · Oct 27, 2026 – Nov 2, 2026 (UTC)
A high-throughput and memory-efficient inference and serving engine for LLMs
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.
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SGLang is a high-performance serving framework for large language models and multimodal models.
Remory: Learning Residual Memory for Context Compaction
🧱 Experimental library for building inference engines
Nano vLLM
LMCache: Supercharge Your LLM with the Fastest KV Cache Layer
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
Ultralytics YOLOv5 in PyTorch for object detection, instance segmentation, classification, training, and export.
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.
PyTorch implementation of YOLOv3, YOLOv3-SPP, and YOLOv3-tiny for real-time object detection with training, validation, inference, and multi-format export.
Making large AI models cheaper, faster and more accessible
Superduper: End-to-end framework for building custom AI applications and agents.