rdma_demo

SGLang is a fast serving framework for large language models and vision language models.

Astron-xmod-shim — Lightweight, declarative middleware for reliably converging AI service workloads.

SGLang is a fast serving framework for large language models and vision language models.

Cross-platform AI workflow DSL converter supporting iFlytek Spark, Dify, and Coze platforms with unified intermediate representation and bidirectional transformation capabilities.

A workload for deploying LLM inference services on Kubernetes

OpenAI ChatGPT, GPT-3, GPT-4, DALL·E, Whisper API wrapper for Go

OME is a Kubernetes operator for enterprise-grade management and serving of Large Language Models (LLMs)

SGLang is a fast serving framework for large language models and vision language models.

This a simple implementation of an MCP server using iFlytek. It enables calling iFlytek workflows through MCP tools.

easy version of pyverbs

A lightweight data processing framework built on DuckDB and 3FS.

Expert Parallelism Load Balancer

Analyze computation-communication overlap in V3/R1.

A bidirectional pipeline parallelism algorithm for computation-communication overlap in DeepSeek V3/R1 training.

My learning notes for ML SYS.

DeepGEMM: clean and efficient FP8 GEMM kernels with fine-grained scaling

DeepEP: an efficient expert-parallel communication library

FlashMLA: Efficient Multi-head Latent Attention Kernels

Production-tested AI infrastructure tools for efficient AGI development and community-driven innovation

SGLang is a fast serving framework for large language models and vision language models.

🪄 Turns your machine learning code into microservices with web API, interactive GUI, and more.

A collection of community maintained NRI plugins

SciLifeLab Serve is a platform offering machine learning model serving, data science app hosting (Shiny, Gradio, Streamlit, Dash, etc.), and other tools to life science researchers affiliated with …

Examples of models deployable with Truss

BERT classification model for processing texts longer than 512 tokens. Text is first divided into smaller chunks and after feeding them to BERT, intermediate results are pooled. The implementation …

A high-throughput and memory-efficient inference and serving engine for LLMs

Llama中文社区，实时汇总最新Llama学习资料，构建最好的中文Llama大模型开源生态，完全开源可商用