AirLLM 2.8T LLM inference with single 4GB GPU
- chinese-nlp
- finetune
- generative-ai
- instruct-gpt
Repository Index
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Enjoy the magic of Diffusion models!
Easy-to-use and powerful LLM and SLM library with awesome model zoo.
- nlp
- embedding
- bert
- ernie
Accessible large language models via k-bit quantization for PyTorch.
- llm
- machine-learning
- pytorch
- qlora
LMDeploy is a toolkit for compressing, deploying, and serving LLMs.
- cuda-kernels
- deepspeed
- fastertransformer
- llm-inference
The central nervous system and backend orchestrator for Fractal. It handles multi-tenant task scheduling, real-time TFLOPs budgeting, and federated weight aggregation using FedAvg.
A unified inference and post-training framework for accelerated video generation.
- diffusers
- diffusion-models
- video-generation
- distillation
Fast inference engine for Transformer models
- neural-machine-translation
- cpp
- mkl
- quantization
A repository for storing models that have been inter-converted between various frameworks. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML.
- tensorflow
- tensorflow-lite
- openvino
- edgetpu
[ICLR2025, ICML2025, NeurIPS2025 Spotlight] Quantized Attention achieves speedup of 2-5x compared to FlashAttention, without losing end-to-end metrics across language, image, and video models.
- attention
- inference-acceleration
- llm
- quantization
State-of-the-art LLM compression, built for production inference with vLLM
- compression
- quantization
TurboDiffusion: 100–200× Acceleration for Video Diffusion Models
- ai-infra
- diffusion-models
- inference-acceleration
- rcm
[MLSys 2024 Best Paper Award] AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
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.
[CVPR 2023] DepGraph: Towards Any Structural Pruning; LLMs, Vision Foundation Models, etc.
- pruning
- model-compression
- efficient-deep-learning
- llm
Pretrained language model and its related optimization techniques developed by Huawei Noah's Ark Lab.
- knowledge-distillation
- model-compression
- quantization
- pretrained-models
4 bits quantization of LLaMA using GPTQ
PyTorch native quantization for training and inference
- brrr
- dtypes
- inference
- mx
An Automatic Model Compression (AutoMC) framework for developing smaller and faster AI applications.
- deep-learning
- model-compression
- mobile-app
- automl
Images to inference with no labeling (use foundation models to train supervised models).
- computer-vision
- model-distillation
- auto-labeling
- deep-learning
AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.
- quantization
- deep-learning
- compression
- open-source
SOTA low-bit LLM quantization (INT8/FP8/MXFP8/INT4/MXFP4/NVFP4) & sparsity; leading model compression techniques on PyTorch, TensorFlow, and ONNX Runtime
- low-precision
- pruning
- sparsity
- auto-tuning
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.
- kv-cache
- llm-inference
- local-llm
- quantization
Olive: Simplify ML Model Finetuning, Conversion, Quantization, and Optimization for CPUs, GPUs and NPUs.