base on null # Latest News
### Recent Updates
- [2025/12] **SimAI 1.5 Released!** This release brings end-to-end simulation for multi-request **inference** workloads. Key features include:
- **Advanced Inference Simulation:** Model complex scenarios with Prefill/Decode separation.
- **Modern Model Support:** Now includes DeepSeek, Qwen3Moe and Qwen3Next. See [AICB's README](./aicb/README.md) for more detailed information.
- **Request Scheduling:** Request scheduling is now handled by a component adapted from Microsoft's [Vidur](https://github.com/microsoft/vidur). See [Vidur-Alibabacloud's README](./vidur-alibabacloud/README.md) for more detailed information.
- [2025/11] [AICB](https://github.com/aliyun/aicb/tree/master) now supports generating **prefill/decode** inference workloads for **DeepSeek**, **Qwen3-MoE** and **Qwen3-Next**.
- [2025/09] [AICB](https://github.com/aliyun/aicb/tree/master) now supports generating training workloads for DeepSeek. Thanks to [@parthpower](https://github.com/parthpower) for this contribution.
- [2025/06] The code of SimCCL is first released in the branch [SimCCL](https://github.com/aliyun/SimAI/tree/SimCCL) and will be released in SimCCL repository soon.
**We warmly welcome contributions from the community!** If you are interested in helping shape the future of SimAI, please feel free to open an issue to discuss your ideas or submit a pull request.
<div align="center">
🎯 <b>Events & Community Engagement</b> 🎯
### 📅 Upcoming Events
| Date | Event | Location | Content | Type |
|:----:|:----- |:-------- |:------- |:----:|
| -- | | | | |
### 🌟 Past Events
| Date | Event | Location | Content | Type |
|:----------------:|:------------------------------------------------------------------------ |:----------------------- |:-------------------------------------------------------- |:-------------:|
| Dec 30, 2025 | SimAI 1.5 | 🌐 Online | The release of SimAI 1.5 | 💻 Virtual |
| Jun 4, 2025 | The first workshop of the SimAI community | 📍 Peking University | Three talks from community contributors | 🎓 On-site |
| May 24, 2025 | The 28th Chinasys workshop | 📍 Chongqing University | An invited talk about SimAI | 🎓 On-site |
| Dec 27, 2024 | SimAI Technical Presentation | 📍 Beihang University | SimAI Technical Sharing & Discussion | 🎓 On-site |
| Dec 6, 2024 | HKUST Technical Workshop | 📍 HKUST(GZ) | SimAI Technical Sharing & Discussion | 🎓 On-site |
| Dec 5, 2024 | [Bench'24 Conference](https://mp.weixin.qq.com/s/STic_E12xMhZRxhzK9wRnw) | 📍 Guangzhou | SimAI Tutorial & Deep-dive Session | 🎓 On-site |
| Nov 26, 2024 | SimAI Community Live Stream | 🌐 Online | Interactive Technical Discussion & Demo (400+ Attendees) | 💻 Virtual |
| Nov 15, 2024 | Technical Workshop | 📍 Thousand Island Lake | SimAI Offline Technical Exchange | 🎯 On-site |
| Oct 18, 2024 | Guest Lecture | 📍 Fudan University | SimAI Tutorial & Public Course | 🎓 On-site |
| Sept 24-26, 2024 | CCF HPC China 2024 | 📍 Wuhan | SimAI Introduction & Technical Presentation | 🎤 Conference |
</div>
---
# Table of Contents
- [SimAI Overview](#simai-overview)
- [Introduction](#introduction)
- [Components](#components)
- [Scenario](#scenario)
- [Citation](#citation)
- [Usage](#usage)
- [Setup](#setup)
- [From Source Code](#from-source-code)
- [Use SimAI-Analytical](#use-simai-analytical)
- [Use SimAI-Simulation](#use-simai-simulation)
- [Use Vidur-AICB](#use-vidur-aicb)
# SimAI Overview
## Introduction
**SimAI** is the industry's first full-stack, high-precision **Sim**ulator for **AI** large-scale **\*\*inference\*\*** and **training**. It provides detailed modeling and simulation of the entire LLM training process, encompassing framework, collective communication, network layers, and more. This comprehensive approach offers end-to-end performance data, enabling researchers to:
- Analyze inference/training process details
- Evaluate the time consumption of AI tasks under specific conditions
- Evaluate E2E performance gains from various algorithmic optimizations including:
- Framework parameters settings
- Collective communication algorithms
- NCCL environment variables
- Network transmission protocols
- Congestion control algorithms
- Adaptive routing algorithms
- Scale-up/out network topology modifications
- ...
## Components
<pre>
|--- <a href="https://github.com/aliyun/aicb">AICB</a>
SimAI --|--- <a href="https://github.com/aliyun/SimCCL">SimCCL</a>
|--- <a href="https://github.com/aliyun/SimAI/tree/master/astra-sim-alibabacloud">astra-sim-alibabacloud</a>
|--- <a href="https://github.com/aliyun/ns-3-alibabacloud">ns-3-alibabacloud</a>
|--- vidur-alibabacloud
</pre>
Building on pure simulation capabilities, SimAI has evolved into a versatile full-stack toolkit comprising four components ([aicb](https://github.com/aliyun/aicb), [SimCCL](https://github.com/aliyun/SimCCL), [astra-sim-alibabacloud](https://github.com/aliyun/SimAI/tree/master/astra-sim-alibabacloud), [ns-3-alibabacloud](https://github.com/aliyun/ns-3-alibabacloud)). These components can be combined in various ways to achieve different functionalities. Below, we present the six main usage scenarios for SimAI. We encourage users to explore even more possibilities with this powerful tool.
Below is the architecture diagram of the SimAI Simulator:
astra-sim-alibabacloud is extended from [astra-sim](https://github.com/astra-sim/astra-sim/tree/ASTRA-sim-1.0). We are grateful to the astra-sim team for their excellent work and open-source contribution. We have integrated NCCL algorithms and added some new features.
## Scenario
SimAI supports three major operation modes to meet different simulation requirements:
**SimAI-Analytical** offers fast simulation by abstracting network communication details using bus bandwidth (busbw) to estimate collective communication time. While it currently supports user-defined busbw, automatic busbw calculation feature is coming soon.
**SimAI-Simulation** provides full-stack simulation with fine-grained network communication modeling. It leverages NS3 or other network simulators (NS3 currently open-sourced) to achieve detailed simulation of all communication behaviors, aiming for high-fidelity reproduction of actual training environments.
**SimAI-Physical** *(Beta)* enables physical traffic generation for CPU RDMA cluster environments. This mode generates NCCL-like traffic patterns, allowing in-depth study of NIC behaviors during LLM training. It is currently in internal testing phase.
| Scenario | Description | Component Combination |
| -------------------------------------- | ------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 1. AICB Test Suite | Run communication patterns on GPU clusters using AICB Test suite | [AICB](https://github.com/aliyun/aicb) |
| 2. AICB/AIOB Workload | Model compute/communication patterns of **\*\*inference\*\*/training** process to generate workload | [AICB](https://github.com/aliyun/aicb) |
| 3. Collective Comm Analyze | Break down collective communication operations into point-to-point communication sets | [SimCCL](https://github.com/aliyun/SimCCL) |
| 4. Collective Comm w/o GPU | Perform RDMA collective communication traffic on non-GPU clusters | [AICB](https://github.com/aliyun/aicb) + [SimCCL](https://github.com/aliyun/SimCCL) + [astra-sim-alibabacloud](https://github.com/aliyun/SimAI/tree/master/astra-sim-alibabacloud)(physical) |
| 5. SimAI-Analytical | Conduct rapid AICB workload analysis and simulation on any server (ignoring underlying network details) | [AICB](https://github.com/aliyun/aicb) + [astra-sim-alibabacloud](https://github.com/aliyun/SimAI/tree/master/astra-sim-alibabacloud)(analytical) |
| 6. SimAI-Simulation | Perform full simulation on any server | [AICB](https://github.com/aliyun/aicb) + [SimCCL](https://github.com/aliyun/SimCCL) + [astra-sim-alibabacloud](https://github.com/aliyun/SimAI/tree/master/astra-sim-alibabacloud)(simulation) + [ns-3-alibabacloud](https://github.com/aliyun/ns-3-alibabacloud) |
| 7. Multi-requests Inference Simulation | Perform full multi-requests **inference** simulation using one GPU server | [AICB](https://github.com/aliyun/aicb) + [SimCCL](https://github.com/aliyun/SimCCL) + [vidur-alibabacloud](./vidur-alibabacloud) + [astra-sim-alibabacloud](https://github.com/aliyun/SimAI/tree/master/astra-sim-alibabacloud)(analytical/simulation) |
## Citation
SimAI work has been accepted by NSDI'25 Spring, for more details, please refer to our paper below:
*SimAI: Unifying Architecture Design and Performance Tuning for Large-Scale Large Language Model Training with Scalability and Precision.*
[[pdf](https://ennanzhai.github.io/pub/nsdi25spring-simai.pdf)] / [[slides](./docs/SimAI_Intro_Online.pdf)] / [[video](https://n.dingtalk.com/dingding/live-room/index.html?roomId=OF5BkBUXVxmgsK7x&liveUuid=305736cd-aa70-498b-8003-2b471a53decd)]
We encourage innovative research and extensions based on SimAI. Welcome to join our community group or reach out via email for discussion. We may provide technical support.
# Quick Start
Here are some simple examples, SimAI full tutorials can be found here: [**SimAI@Tutorial**](./docs/Tutorial.md), [**aicb@Tutorial**](https://github.com/aliyun/aicb/blob/master/training/tutorial.md), [SimCCL@Tutorial], [ns-3-alibabacloud@Tutorial]
## Setup
You can follow the instrucitons below to quickly set up the environtments and run SimAI
### From Source Code
The following code has been successfully tested on GCC/G++ 9.4.0, python 3.8.10 in Ubuntu 20.04
You can use the official Ubuntu 20.04 image, and do not install ninja.
(For generation workloads, it's recommended to leverage NGC container images directly.)
```bash
# Clone the repository
$ git clone https://github.com/aliyun/SimAI.git
$ cd ./SimAI/
# Clone submodules
$ git submodule update --init --recursive
# Make sure use the newest commit
$ git submodule update --remote
# Compile SimAI-Analytical
$ ./scripts/build.sh -c analytical
# Compile SimAI-Simulation (ns3)
$ ./scripts/build.sh -c ns3
```
## Use SimAI-Analytical
```bash
$ ./bin/SimAI_analytical -w example/workload_analytical.txt -g 9216 -g_p_s 8 -r test- -busbw example/busbw.yaml
```
For calculating bus bandwidth autolly, please try the following command:
```bash
$ ./bin/SimAI_analytical -w ./example/workload_analytical.txt -g 9216 -nv 360 -nic 48.5 -n_p_s 8 -g_p_s 8 -r example-
```
## Use SimAI-Simulation
```bash
# Create network topo
$ python3 ./astra-sim-alibabacloud/inputs/topo/gen_Topo_Template.py -topo Spectrum-X -g 128 -gt A100 -bw 100Gbps -nvbw 2400Gbps
# Running
$ AS_SEND_LAT=3 AS_NVLS_ENABLE=1 ./bin/SimAI_simulator -t 16 -w ./example/microAllReduce.txt -n ./Spectrum-X_128g_8gps_100Gbps_A100 -c astra-sim-alibabacloud/inputs/config/SimAI.conf
```
## Use Multi-requests Inference Simulation
For detailed information, please refer to the [README](./vidur-alibabacloud/README.md) file in the `vidur-alibabacloud` directory. This module leverages AICB to profile the computation time of **inference** workloads. Due to its reliance on specific hardware-accelerated libraries like DeepGEMM and FlashMLA, it is exclusively compatible with NVIDIA GPUs based on the **Hopper (SM90)** and **Blackwell (SM100)** architectures.
```shell
# Build from Dockerfile
docker build -t image:latest .
docker run --gpus all -it --rm image:latest
```
**Note**: please add `ENV FLASH_MLA_DISABLE_SM100=1` to Dockerfile if using Hopper GPUs.
# Acknowledgments
A huge thanks to the following people and organizations who have contributed to this project:
- TianHao Fu (Peking University) and [TELOS-syslab](https://github.com/TELOS-syslab/),
- Parth Parikh (KEYSIGHT),
- Sarah-Michelle Hammer & Ziyi Wang (TU-Berlin),
- Xinyue Li (BUPT),
- Tong Chen (Zhejiang University),
- Ming Wang (BUPT),
- Tao Jiang (Institute of Computing Technology, Chinese Academy of Sciences),
and many other individual contributors from the community (See the [Contributors to aliyun/SimAI · GitHub](https://github.com/aliyun/SimAI/graphs/contributors)).
We also thank Chenning Li (MIT CSAIL) who initiated the cooperation on integrating SimAI into [M4](https://github.com/netiken/m4), a new, innovative simulator.
<u>**This project still welcomes more contributions and suggestions**</u>.
# Contact us
Please email Gang Lu (
[email protected]), Feiyang Xue (
[email protected]) or Qingxu Li (
[email protected]) if you have any questions.
Welcome to join the SimAI community chat groups, with the DingTalk group on the left and the WeChat group on the right.
<div style="display: flex; justify-content: flex-start; align-items: center; gap: 20px; margin-left: 20px;">
<img src="./docs/images/simai_dingtalk.jpg" alt="SimAI DingTalk" style="width: 300px; height: auto;">
<img src="./docs/images/simai_wechat.jpeg" alt="SimAI WeChat" style="width: 300px; height: auto;">
</div>
<br/>
", Assign "at most 3 tags" to the expected json: {"id":"12767","tags":[]} "only from the tags list I provide: [{"id":39,"name":"3d-generation","display_name":"3D generation","slug":"3d-generation"},{"id":3,"name":"ai-agent","display_name":"AI agent","slug":"ai-agent"},{"id":8,"name":"ai-coding","display_name":"AI coding assistant","slug":"ai-coding"},{"id":5,"name":"ai-image","display_name":"AI image generation","slug":"ai-image"},{"id":9,"name":"ai-infrastructure","display_name":"AI infrastructure","slug":"ai-infrastructure"},{"id":10,"name":"ai-memory","display_name":"AI memory","slug":"ai-memory"},{"id":11,"name":"ai-skills","display_name":"AI skills","slug":"ai-skills"},{"id":12,"name":"ai-translation","display_name":"AI translation","slug":"ai-translation"},{"id":6,"name":"ai-video","display_name":"AI video generation","slug":"ai-video"},{"id":4,"name":"ai-voice","display_name":"AI voice","slug":"ai-voice"},{"id":7,"name":"ai-workflow","display_name":"AI workflow","slug":"ai-workflow"},{"id":22,"name":"audio-processing","display_name":"Audio processing","slug":"audio-processing"},{"id":29,"name":"authentication","display_name":"Authentication","slug":"authentication"},{"id":51,"name":"bundler","display_name":"Bundler","slug":"bundler"},{"id":41,"name":"chatbot","display_name":"Chatbot","slug":"chatbot"},{"id":27,"name":"cloud-native","display_name":"Cloud native","slug":"cloud-native"},{"id":1,"name":"computer-vision","display_name":"Computer vision","slug":"computer-vision"},{"id":37,"name":"crypto-trading","display_name":"Crypto trading","slug":"crypto-trading"},{"id":57,"name":"curated-list","display_name":"Curated list","slug":"curated-list"},{"id":54,"name":"data-streaming","display_name":"Data streaming","slug":"data-streaming"},{"id":35,"name":"data-visualization","display_name":"Data visualization","slug":"data-visualization"},{"id":16,"name":"database-backup","display_name":"Database backup","slug":"database-backup"},{"id":49,"name":"design-system","display_name":"Design system","slug":"design-system"},{"id":38,"name":"digital-human","display_name":"Digital human","slug":"digital-human"},{"id":34,"name":"document-processing","display_name":"Document processing","slug":"document-processing"},{"id":44,"name":"ecommerce","display_name":"E-commerce","slug":"ecommerce"},{"id":45,"name":"emulator","display_name":"Emulator","slug":"emulator"},{"id":46,"name":"file-management","display_name":"File management","slug":"file-management"},{"id":32,"name":"fintech","display_name":"Fintech","slug":"fintech"},{"id":31,"name":"game-development","display_name":"Game development","slug":"game-development"},{"id":24,"name":"headless-browser","display_name":"Headless browser","slug":"headless-browser"},{"id":52,"name":"headless-cms","display_name":"Headless CMS","slug":"headless-cms"},{"id":36,"name":"home-automation","display_name":"Home automation","slug":"home-automation"},{"id":20,"name":"image-editing","display_name":"Image editing","slug":"image-editing"},{"id":28,"name":"iot","display_name":"IoT","slug":"iot"},{"id":13,"name":"local-llm","display_name":"Local LLM","slug":"local-llm"},{"id":17,"name":"mcp","display_name":"MCP","slug":"mcp"},{"id":47,"name":"monitoring","display_name":"Monitoring","slug":"monitoring"},{"id":2,"name":"nlp","display_name":"NLP","slug":"nlp"},{"id":26,"name":"observability","display_name":"Observability","slug":"observability"},{"id":40,"name":"pentesting","display_name":"Pentesting","slug":"pentesting"},{"id":48,"name":"programming-examples","display_name":"Programming examples","slug":"programming-examples"},{"id":42,"name":"proxy","display_name":"Proxy","slug":"proxy"},{"id":14,"name":"rag","display_name":"RAG","slug":"rag"},{"id":56,"name":"resume-building","display_name":"Resume building","slug":"resume-building"},{"id":33,"name":"robotics","display_name":"Robotics","slug":"robotics"},{"id":30,"name":"search","display_name":"Search","slug":"search"},{"id":43,"name":"self-hosted","display_name":"Self-hosted","slug":"self-hosted"},{"id":50,"name":"static-analysis","display_name":"Static analysis","slug":"static-analysis"},{"id":18,"name":"synthetic-data","display_name":"Synthetic data","slug":"synthetic-data"},{"id":19,"name":"text-to-speech","display_name":"Text to speech","slug":"text-to-speech"},{"id":53,"name":"ui-components","display_name":"UI components","slug":"ui-components"},{"id":15,"name":"vector-database","display_name":"Vector database","slug":"vector-database"},{"id":21,"name":"video-editing","display_name":"Video editing","slug":"video-editing"},{"id":25,"name":"web-scraping","display_name":"Web scraping","slug":"web-scraping"},{"id":55,"name":"webassembly","display_name":"WebAssembly","slug":"webassembly"},{"id":23,"name":"workflow-automation","display_name":"Workflow automation","slug":"workflow-automation"}]" returns me the "expected json"