AI Compute
AI Application
Research
AI Compute

AI-Optimized Cloud for Performance and Experience

Build, train, and deploy models with cloud-native experience, designed for performance and global scalability.

User Command Line & HTTP API

Flexible user control via CLI and API integration.

Web Dashboard

Intuitive web interface for real-time monitoring and management.

SING Cloud Architecture
AI Cloud Interface

User Command Line & HTTP API

Web Dashboard

Image

Pre-configured environments optimized for AI and machine learning tasks.

Job

Efficient task execution and management for large-scale workloads.

Service

Scalable backend services for AI infrastructure and operations.

Global Resource Network

Cluster Job Scheduler

Advanced job scheduling to optimize resource utilization.

Network Overlay

Secure, high-performance virtual networking for cloud clusters.

AI-centric Networking

Optimized network infrastructure designed for AI workloads.

GPU Hardware

High-performance and affordable GPUs for demanding AI applications.

Unified Data Storage

Centralized, scalable storage solution for AI and cloud data management.

AI Application

Effortless AI App Development

Visual workflows, agent tools, and user-friendly API, all in one place for fast AI integration.

End-to-end AI Workflows

Design AI workflows visually, from data to model integration, and use APIs to integrate directly into your app.

Comprehensive Model Support

Connect and manage language and vision models from various providers.

Seamless Knowledge Integration

Link with external data sources, enabling efficient knowledge retrieval

Autonomous AI Agents Capabilities

Create and integrate intelligent agents with built-in or custom tools to automate complex tasks.

Research & Solution

We research efficient cluster management solution optimized for

AI applications in large-scale GPU clusters

Pioneering AI Infrastructure Research

We lead the way in developing cutting-edge cluster management solutions optimized for AI applications. Our research drives performance, scalability, and reliability in large-scale GPU clusters.

Kaiqiang Xuwebsite
Founder and CEO, SING Cloud
Kaiqiang's research improves the efficiency and accessibility of machine learning systems, and publishes in top-tier conferences.
Read Our Research Papers
AI Infrastructure
Design and Operation of Shared Machine Learning Clusters on Campus (ASPLOS 2025)
Kaiqiang Xu, Decang Sun, Hao Wang, Zhenghang Ren, Xinchen Wan, Xudong Liao, Zilong Wang, Junxue Zhang, Kai Chen
AI-centric Networking
Towards Domain-Specific Network Transport for Distributed DNN Training (NSDI 2024)
Hao Wang, Han Tian, Jingrong Chen, Xinchen Wan, Jiacheng Xia, Gaoxiong Zeng, Wei Bai, Junchen Jiang, Yong Wang, Kai Chen
ML System
Accelerating Neural Recommendation Training with Embedding Scheduling (NSDI 2024)
Chaoliang Zeng, Xudong Liao, Xiaodian Cheng, Han Tian, Xinchen Wan, Hao Wang, Kai Chen
ML Systems
An Efficient Multi-Level Inference System for Large Language Models (EuroSys 2023)
Yiding Wang, Kai Chen, Haisheng Tan, Kun Guo

Embrace an AI-Optimized Cluster Solution

Boost your on-premise infrastructure with SING, offering 24/7 hardware monitoring, streamlined task management, efficient job scheduling, and cutting-edge ML optimization for maximum performance and reliability.

Explore On-premise Deployment

High Stability

SING enhances operational reliability by providing continuous 24/7 hardware monitoring, robust containerization, and efficient job scheduling.

Enhanced Usability

SING simplifies resource provisioning with an user-friendly, AI-centric workflow, where users effortlessly manage tasks and services through command line, web UI, or API.

Maximized Performance

SING incorporates state-of-the-art ML systems and network technologies from the academic sector, tailored for maximizing performance and scalability in AI applications.