OnPair: Short Strings Compression for Fast Random Access

A fast framework for writing baseline compiler back-ends in C++

OpenResty's Branch of LuaJIT 2

Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

A machine learning compiler for GPUs, CPUs, and ML accelerators

Implement a reasoning LLM in PyTorch from scratch, step by step

Build smaller, faster, and more secure desktop and mobile applications with a web frontend.

AnyBlox runtime and tooling

An extensible, state-of-the-art framework for columnar compression, and the fastest FOSS columnar file format. Formerly at @spiraldb, now an Incubation Stage project at LFAI&Data, part of the Linux…

An easy-to-use, header-only C++ wrapper for Linux' perf event API

GPU-native composable analytics engine

Goal: Enable awesome tooling for Bazel users of the C language family.

The universal proxy platform

[TMLR 2025] Efficient Reasoning Models: A Survey

Vector (and Scalar) Quantization, in Pytorch

Official repository of the xLSTM.

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…

The hub for EleutherAI's work on interpretability and learning dynamics

Modeling, training, eval, and inference code for OLMo

InkFuse - An Experimental Database Runtime Unifying Vectorized and Compiled Query Execution.

CUDA Templates and Python DSLs for High-Performance Linear Algebra

TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. Tensor…

Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)

Local UI to run and train LLMs and diffusion models. Supports GGUF, MLX, Qwen3.8, DeepSeek-V4, MiniMax-H3, Gemma 4, FLUX and more.

The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.

A Flexible Framework for Experiencing Heterogeneous LLM Inference/Fine-tune Optimizations

FP16xINT4 LLM inference kernel that can achieve near-ideal ~4x speedups up to medium batchsizes of 16-32 tokens.

A concise but complete full-attention transformer with a set of promising experimental features from various papers