AI-Stack│an AI infrastructure and GPU resource scheduling platform│Infinitix Inc.

AI-Stack

10x ROI in AI

Leave the complicated GPU infrastructure to us, and focus on training your AI model.
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AI-Stack: GPU resource scheduling and AI Infrastructure Management Platform

AI-Stack is the industry-leading AI infrastructure management software and an essential tool for enterprises adopting AI services. It integrates GPU partitioning (NVIDIA/AMD), GPU aggregation, cross-node computing, an intuitive user interface, containerization and MLOps workflows, open-source deep learning tools, and environment deployment features.

Enhance your investment value

AI-Stack provides highly flexible and scalable solutions for enterprises and teams. With a single platform, you can quickly implement various AI service needs while simultaneously solving common problems encountered in hardware resource management and AI development deployment. This maximizes the return on your AI investment, achieving up to a 10x ROI according to customer feedback.

#AI Transformation  #GPU Resource Management

Develop you AI services on AI-Stack >>
5-fold GPU utilization
Leverage AI-Stack's GPU partitioning technology to immediately end compute waste and uneven resource allocation! Boost GPU utilization from an inefficient 30% to an impressive 90%, maximizing your return on expensive hardware investment.
#GPU Partitioning #GPU Utilization
Learn more about GPU slicing
Innumerable GPU workloads
The intelligent dynamic resource scheduling technology divides GPU’s memory and cores for simultaneous use among users, enabling concurrent execution of numerous jobs and flexible computing of increasing data.
#Dynamic resource scheduling #simultaneous use among users
Read more about the control plane
Set up AI dev environment within 1 min
Based on the Kubernetes architecture and integrated with pre-configured open-source development frameworks and containerization technology, you can quickly set up an AI development environment in one minute. This drastically reduces the configuration burden on IT staff, allowing development teams to start projects immediately and maximize workflow efficiency.
#Simplify dev environment #Reduce setup time
Learn how to set up AI dev environment

Encompass all AI management tools, including hardware and software

AI-Stack is a one-stop-shop platform providing comprehensive services across physical hardware to software programs and platforms. It enables AI infrastructure management, project development, workflow supervision, resource allocation, model training, group collaboration, model inference, and more, as scalable and expandable solutions for enterprises and project teams.

Development and
Ecosystem Layers
AI Development Ecosystem Layer
Covering IDEs, training frameworks, HPC, large language models, experiment tracking, workflow orchestration, and model inference services. It enables efficient AI/ML pipelines with end-to-end support from development to deployment, empowering data scientists to focus on innovation and accelerate value creation.
Control Plane
AI-Stack Control Plane Layer
Provides GPU resource partitioning and multi-tenant management to maximize GPU utilization; supports custom images and batch job scheduling to accelerate AI development and deployment; seamlessly integrates with Kubernetes to optimize AI workload orchestration.
Infrastructure Cluster
Infrastructure Cluster Layer
Manage both NVIDIA and AMD GPU servers on a single platform to build a high-performance AI computing environment, with support for BeeGFS, Ceph, and other storage architectures to ensure efficient data flow.
AI Developer Ecosystem Layer
Covering IDEs, training frameworks, HPC, large language models, experiment tracking, workflow orchestration, and model inference services. It enables efficient AI/ML pipelines with end-to-end support from development to deployment, empowering data scientists to focus on innovation and accelerate value creation.
AI-Stack Control Plane Layer
Provides GPU resource partitioning and multi-tenant management to maximize GPU utilization; supports custom images and batch job scheduling to accelerate AI development and deployment; seamlessly integrates with Kubernetes to optimize AI workload orchestration.
Infrastructure Cluster Layer
Manage both NVIDIA and AMD GPU servers on a single platform to build a high-performance AI computing environment, with support for BeeGFS, Ceph, and other storage architectures to ensure efficient data flow.

An AI management and operation platform applicable across fields

Successful cases of AI-Stack have been implemented in many industries with endorsements from enterprises and organizations to fulfill diverse requirements of GPU management and create enterprise-specific models.

  • Data Center
  • Semiconductor
  • Optoelectronics
  • Manufacturing
  • Finance
  • Energy
  • Transportation
  • Video Surveillance
  • Pharmaceuticals
  • Academic Research
  • Education • Government Agencies
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AI-Stack makes good use of GPU resources to the utmost efficiency and eliminates the complicated follow-up management processes.
Zong-Ren Wang
Zong-Ren Wang assistant to the Chairman of Himax Technologies
nfinitix has been enthusiastic about fields of AI and considerately engages in helping us go deep into the AI applications and environment of NCKU and launch the system smoothly.
Processor Pau-Choo Chung
Processor Pau-Choo Chungformer Director of the Computer and Network Center at NCKU
Infinitix’s AI-Stack arranges the priority of using DGX machines for AI engineers to avoid resource panic and maximize the benefits.
Pin-An Wu
Pin-An WuVice President of Hualien Tzu Chi Hospital
Thanks to Infinitix Corporation's sincere approach, we were able to obtain satisfactory solutions to the challenging tasks presented by our school one after another. We are very satisfied with Infinitix Corporation's services.
Professor Wang Yongzhong
Professor Wang YongzhongDirector of the Computer Network Center, Taipei University of Technology
Through the comprehensive functional design (of AI-Stack), it is expected to appropriately meet task demand distribution and prevent resource misuse. We control resource utilization efficiency in terms of cost and time.
Professor Chen Qizhang
Professor Chen QizhangProfessor and Head of Department of Information Engineering, Director of Institute I-Shou University

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