Siten Heli

What is Siten Heli?

Stenheli is a provider of artificial intelligence infrastructure solutions; its services include AI servers, GPU-based cloud computing power, training management software, as well as related operation and maintenance services.

It is not a chatbot designed for ordinary users; rather, it provides computational infrastructure for model training, inference, scientific computing, and enterprise-specific private projects.

The main products of Stenhe Lihe

AI servers and workstations

The official website offers artificial intelligence servers, general-purpose servers, storage servers, and water-cooled workstations, which can be used for deep learning training, inference, and high-performance computing.

• Multi-GPU server and computing nodes
• Computing resources suitable for model training and batch inference
• Storage server designed for data storage and fast reading.
• Custom liquid cooling solutions tailored to server room density and cooling requirements

Siten Cloud Computing Power

StenCloud offers remote computing power to users who do not wish to set up their own data centers; the team can select the appropriate computing cards, duration, storage, and network resources based on the specific tasks at hand.

The actual performance of cloud computing resources depends on the GPU model, video memory, CPU, storage speed, network connectivity, and the level of resource exclusivity; it is not sufficient to consider only the price.

AI Open Platform

The AI open platform is used for the unified management of data, training tasks, models, images, as well as storage and computing resources; it is suitable for multiple users to share GPU clusters.

• Data annotation and dataset management
• Creation of AI training tasks and resource scheduling
• Model factory and model asset management
• Task image creation and image center
• Operations, storage, costs, and user management

Stenyun Management Platform

The cloud management platform is designed for the management of computing, storage, and networking resources, and it helps service providers or enterprises establish a unified system for resource allocation and order management.

It is more similar to an AI cloud infrastructure console; before deployment, it is necessary to plan in conjunction with the existing data centers, account systems, networking setup, and billing rules.

SCM Artificial Intelligence Cloud Platform

The SCM Artificial Intelligence Cloud Platform is part of Stenhele’s portfolio of software products; it is used to organize artificial intelligence computing resources and related cloud services.

The functional boundaries and delivery methods may vary depending on the version of the software; therefore, when purchasing such software, companies should request a list of the functions available in the current version.

Xunsī Code Generation All-in-One Machine

The official help center also lists the Xunsisi code generation all-in-one system, illustrating how its products have evolved from providing general computing power to offering all-in-one solutions for large-model applications.

A turnkey project typically includes hardware, models, software, and implementation services; the specific capabilities of the models and the scope of authorization depend on the details of the project.

Core capabilities of the AI open platform

Training tasks and resource scheduling

Administrators can add heterogeneous computing cards to the resource pool, and then allocate GPUs, storage, and runtime environments based on the needs of users, projects, and tasks.

• Unified creation and viewing of training tasks
• Configure tasks based on computing resource specifications
• Record operation status and cost orders
• Through the image reuse framework and dependency environment

Model and Image Management

The Model Factory is used for centrally storing and managing model assets, while the Image Center helps teams reuse proven training and inference environments.

Such capabilities can help reduce issues related to environmental inconsistencies, but the team still needs to maintain records of model versions, data versions, and experimental parameters.

Storage and operation and maintenance

The platform integrates data storage, device status, task execution, and fault handling under a single control interface, enabling administrators to monitor the usage of the cluster.

• Configure data storage devices and capacity
• Calculate and store resources based on task associations
• Monitor the operation status of platforms and devices
• Assist with after-sales fault diagnosis and maintenance.

How to use Sitenhe Lihe

Before deploying in a business environment, it is necessary to determine the scale of the model, the training method, and the concurrent processing requirements, and then choose between hardware, cloud computing resources, or a hybrid approach.

1. Identify the requirements for training, inference, and data storage.
2. Verify model framework, driver, and GPU compatibility
3. Choose to purchase servers, rent cloud computing resources, or use a private platform.
4. Plan user, project, network, and storage permissions
5. Create training images and upload data and models.
6. Create small-scale tasks to verify performance and stability.
7. Scale up gradually based on monitoring, costs, and utilization rates

Performance verification prior to procurement

• Use real models to test video memory and throughput.
• Measurement data loading and distributed communication speed
• Verify compatibility with mainstream frameworks, drivers, and containers.
• Verify the failover, backup, and recovery processes.
• Compare long-term utilization rate with total cost of ownership

Prices and packages

According to the official documentation, the platform allows for separate billing for training tasks, storage, and network bandwidth; it also supports real-time billing as well as periodic billing options such as per-day and per-month plans.

Discount plans can be set on an hourly, daily, monthly, or annual basis; the final price is influenced by the user’s level, the location of the resources, the model, any ongoing promotions, and the contract terms.

Supported platforms

• Web version or official online portal

The client, region, language, and entry point may change with different versions; it is necessary to refer to the current product page before installation or payment.

APIs, SDKs, and open-source status

The official GitHub organization of Stenhe Li has made available repositories related to the construction of task images for AI open platforms, as well as documentation and knowledge bases; it also includes several branches of upstream projects.

Different warehouses use licenses such as Apache and GPL; therefore, it cannot be concluded that the entire AI open-platform, cloud services, or hardware solutions are entirely open source.

Which users are it suitable for

• Universities and research institutions: establishing shared GPU training platforms
• AI companies: management of model training, inference, and data resources
• AI Computing Center: Provides heterogeneous computing power and billing services.
• Traditional enterprises: Implementing projects for privatized large models and knowledge bases
• Development team: Rent GPUs as needed to carry out experiments and deployments

Product advantages

• Hardware, cloud computing power, platform software, and maintenance services form a complete solution.
• Covers training, models, images, storage, and cost management.
• Supports shared computing infrastructure for multiple users and projects.
• It possesses capabilities for server provision, liquid cooling, and cluster deployment.
• Provides public help documentation and some GitHub projects

Usage restrictions and precautions

• The platform is designed for professional infrastructure, and the barriers to its deployment and operation are relatively high.
• Hardware performance must be tested using real workloads.
• The fixed price is not disclosed; when comparing different suppliers, the specifications must be standardized.
• An open-source repository does not mean that the entire source code of a commercial platform is made available.
• Security and authorization must be implemented separately for training data and models.
• Before making a purchase, it is necessary to clarify the scope of maintenance, spare parts, and fault response.

Basic information

Recommendation score

Recommendation score: 4.2/5.

Stenheli is suitable for organizations that need to build or operate AI computing platforms; its focus is on resource management and infrastructure provision, rather than directly generating content.

Before making a choice, it is necessary to conduct tests on performance, compatibility, and cost using the actual model, and to evaluate hardware, software, storage, networking, and maintenance as a whole.

Frequently Asked Questions

What kind of platform is Sitenhe Lihe?

Stenheli is a provider of AI infrastructure solutions, offering GPU servers, cloud computing power, AI training management software, as well as storage and maintenance services.

Is Stenheli suitable for ordinary individual users?

It is primarily aimed at enterprises, research institutions, and development teams; ordinary users should use it only when they need GPU computing power, a training platform, or a private deployment solution.

What can the Sitenheli AI open platform do?

The platform covers infrastructure tasks such as data annotation, AI training, model management, image management, storage, operations and maintenance, billing, and user management.

How does Stenhe Li He charge?

The cost is determined by factors such as the model of the computing card, the region, the duration of the task, as well as storage and network resources. Billing can be done in real time or on a daily or monthly basis; the amount to be paid is as specified in the platform’s or supplier’s quotes.

Does Stenhe Li provide private deployment options?

The official website highlights integrated infrastructure and customized services; the specific scale of privatization, delivery methods, and maintenance conditions need to be confirmed with the official sales team.

Does Stenhe Li have any open-source projects?

Yes, the official GitHub organization has made available repositories for task image building and knowledge bases, but the entire commercial cloud platform and hardware solutions are not entirely open source.

What should be checked before choosing Sitenhe Li?

It is necessary to verify the GPU model, video memory, compatibility of drivers and frameworks, storage network, billing cycle, data security, after-sales support, and migration solutions.