FlagOpen / FlagGems
FlagGems is an operator library for large language models implemented in Triton Language.
☆467Updated this week
Alternatives and similar repositories for FlagGems:
Users that are interested in FlagGems are comparing it to the libraries listed below
- A collection of memory efficient attention operators implemented in the Triton language.☆257Updated 9 months ago
- Puzzles for learning Triton, play it with minimal environment configuration!☆267Updated 4 months ago
- flash attention tutorial written in python, triton, cuda, cutlass☆315Updated 3 months ago
- A Easy-to-understand TensorOp Matmul Tutorial☆332Updated 6 months ago
- Development repository for the Triton-Linalg conversion☆182Updated last month
- Several optimization methods of half-precision general matrix multiplication (HGEMM) using tensor core with WMMA API and MMA PTX instruct…☆374Updated 6 months ago
- Materials for learning SGLang☆360Updated last week
- Domain-specific language designed to streamline the development of high-performance GPU/CPU/Accelerators kernels☆858Updated this week
- Dynamic Memory Management for Serving LLMs without PagedAttention☆333Updated last week
- BitBLAS is a library to support mixed-precision matrix multiplications, especially for quantized LLM deployment.☆566Updated last month
- ☆139Updated 11 months ago
- learning how CUDA works☆226Updated last month
- ☆145Updated 2 months ago
- AI Accelerator Benchmark focuses on evaluating AI Accelerators from a practical production perspective, including the ease of use and ver…☆232Updated last week
- A model compilation solution for various hardware☆416Updated 2 weeks ago
- Disaggregated serving system for Large Language Models (LLMs).☆529Updated 7 months ago
- [EMNLP 2024 Industry Track] This is the official PyTorch implementation of "LLMC: Benchmarking Large Language Model Quantization with a V…☆443Updated this week
- USP: Unified (a.k.a. Hybrid, 2D) Sequence Parallel Attention for Long Context Transformers Model Training and Inference☆463Updated 2 weeks ago
- how to learn PyTorch and OneFlow☆417Updated last year
- Zero Bubble Pipeline Parallelism☆379Updated 3 weeks ago
- GLake: optimizing GPU memory management and IO transmission.☆451Updated last week
- Shared Middle-Layer for Triton Compilation☆236Updated this week
- Examples of CUDA implementations by Cutlass CuTe☆148Updated 2 months ago
- Yinghan's Code Sample☆316Updated 2 years ago
- optimized BERT transformer inference on NVIDIA GPU. https://arxiv.org/abs/2210.03052☆471Updated last year
- 📚FFPA(Split-D): Yet another Faster Flash Prefill Attention with O(1) GPU SRAM complexity for headdim > 256, ~2x↑🎉vs SDPA EA.☆157Updated last week
- FlagScale is a large model toolkit based on open-sourced projects.☆257Updated last week
- A simple high performance CUDA GEMM implementation.☆357Updated last year
- ☆193Updated 8 months ago
- [MLSys'25] QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving; [MLSys'25] LServe: Efficient Long-sequence LLM Se…☆624Updated 3 weeks ago