Alternatives and similar repositories for Triton-distributed

Users that are interested in Triton-distributed are comparing it to the libraries listed below

• bytedance / flux
  A fast communication-overlapping library for tensor/expert parallelism on GPUs.
  ☆1,167Updated 2 months ago
• SiriusNEO / Triton-Puzzles-Lite
  Puzzles for learning Triton, play it with minimal environment configuration!
  ☆560Updated 2 months ago
• flagos-ai / FlagGems
  FlagGems is an operator library for large language models implemented in the Triton Language.
  ☆763Updated this week
• perplexityai / pplx-kernels
  Perplexity GPU Kernels
  ☆529Updated 2 weeks ago
• volcengine / veScale
  A PyTorch Native LLM Training Framework
  ☆884Updated 2 months ago
• Dao-AILab / quack
  A Quirky Assortment of CuTe Kernels
  ☆660Updated 3 weeks ago
• LLMServe / DistServe
  Disaggregated serving system for Large Language Models (LLMs).
  ☆729Updated 7 months ago
• sail-sg / zero-bubble-pipeline-parallelism
  Zero Bubble Pipeline Parallelism
  ☆435Updated 6 months ago
• 66RING / tiny-flash-attention
  flash attention tutorial written in python, triton, cuda, cutlass
  ☆448Updated 6 months ago
• sgl-project / sgl-learning-materials
  Materials for learning SGLang
  ☆650Updated this week
• microsoft / vattention
  Dynamic Memory Management for Serving LLMs without PagedAttention
  ☆436Updated 5 months ago
• sgl-project / SpecForge
  Train speculative decoding models effortlessly and port them smoothly to SGLang serving.
  ☆483Updated this week
• efeslab / Nanoflow
  A throughput-oriented high-performance serving framework for LLMs
  ☆915Updated 3 weeks ago
• mit-han-lab / omniserve
  [MLSys'25] QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving; [MLSys'25] LServe: Efficient Long-sequence LLM Se…
  ☆781Updated 8 months ago
• microsoft / BitBLAS
  BitBLAS is a library to support mixed-precision matrix multiplications, especially for quantized LLM deployment.
  ☆709Updated 3 months ago
• KnowingNothing / MatmulTutorial
  A Easy-to-understand TensorOp Matmul Tutorial
  ☆393Updated last month
• ScalingIntelligence / KernelBench
  KernelBench: Can LLMs Write GPU Kernels? - Benchmark with Torch -> CUDA (+ more DSLs)
  ☆663Updated last week
• hahnyuan / LLM-Viewer
  Analyze the inference of Large Language Models (LLMs). Analyze aspects like computation, storage, transmission, and hardware roofline mod…
  ☆578Updated last year
• pranjalssh / fast.cu
  Fastest kernels written from scratch
  ☆394Updated 2 months ago
• Bruce-Lee-LY / cuda_hgemm
  Several optimization methods of half-precision general matrix multiplication (HGEMM) using tensor core with WMMA API and MMA PTX instruct…
  ☆499Updated last year
• mirage-project / mirage
  Mirage Persistent Kernel: Compiling LLMs into a MegaKernel
  ☆1,951Updated this week
• HazyResearch / Megakernels
  kernels, of the mega variety
  ☆608Updated last month
• AlibabaPAI / llumnix
  Efficient and easy multi-instance LLM serving
  ☆510Updated 2 months ago
• microsoft / sarathi-serve
  A low-latency & high-throughput serving engine for LLMs
  ☆445Updated last month
• antgroup / glake
  GLake: optimizing GPU memory management and IO transmission.
  ☆489Updated 7 months ago
• flagos-ai / FlagAttention
  A collection of memory efficient attention operators implemented in the Triton language.
  ☆284Updated last year
• thuml / depyf
  depyf is a tool to help you understand and adapt to PyTorch compiler torch.compile.
  ☆760Updated last month
• microsoft / mscclpp
  MSCCL++: A GPU-driven communication stack for scalable AI applications
  ☆437Updated this week
• feifeibear / long-context-attention
  USP: Unified (a.k.a. Hybrid, 2D) Sequence Parallel Attention for Long Context Transformers Model Training and Inference
  ☆602Updated last month
• stepfun-ai / StepMesh
  ☆316Updated last week