AI Computing Power is suitable for individuals and teams to quickly verify and implement.

AIComputingPower

Core parameters and statistics

Project	Specifications
Product Name	AI Computing Power
Category	AI Agent
Delivery form	Web/SaaS
Support Platform	Web
Supported languages	Chinese, English
Supported GPU types	NVIDIA A100, V100, RTX 4090, etc.
Billing method	Hourly / Daily / Monthly
Supported frameworks	PyTorch, TensorFlow, JAX
Target users	AI developers, scientific researchers, small and medium-sized teams
User scale	Undisclosed
Pricing model	Pay as you go

AI Computing Power is a platform that provides cloud AI computing power rental and management services, solving the problem of obtaining computing power for individual developers and small and medium-sized teams in AI model training and inference. The platform aggregates a variety of GPU resources and supports on-demand billing. Users do not need to purchase GPU servers or configure driver environments by themselves. They only need to select computing power specifications on the web page to start computing instances preconfigured with commonly used deep learning frameworks within minutes.

User and market recognition

With the explosive growth of large model training and inference requirements, GPU computing power has become a core bottleneck in AI development. AI Computing Power cuts into this supply and demand gap—providing on-demand computing power to individuals and teams who can’t afford or don’t want to manage their own GPU hardware. Typical users include independent developers performing model fine-tuning, start-up teams requiring batch inference tasks, and academic research groups with fluctuating computing power needs.

At present, the platform has not disclosed the number of users or corporate cooperation cases. An objective assessment recommends focusing on GPU availability, average boot time, and price competitiveness.

Cost advantage

The hardware cost of purchasing a high-end GPU plus supporting host, cooling and electricity costs, the total investment is usually tens of thousands of yuan. AI Computing Power converts fixed capital expenditures into variable costs billed by the hour.

Cost Dimension	Description
Rental on demand	Billed by hour/day, stop when used up
No hardware investment required	No need to purchase a GPU or build a server environment
Free trial	Free trial quota provided for new users after registration

For individual developers and academic researchers with discontinuous computing power needs, the rental model is far more economical than self-purchased hardware. Long-term training teams do not need to lock in large hardware budgets in advance and can flexibly expand capacity according to project progress.

Main functions

• Multiple GPU Selection: Provides a variety of GPU instances from consumer level to enterprise level. Users can choose the computing power level and video memory capacity based on task requirements.
• Pre-configured environment: The image has built-in mainstream deep learning frameworks and CUDA tool chains, ready to use upon startup.
• File Management: Supports data upload, code synchronization and result download through web page or command line tools.
• Run Monitor: Displays GPU utilization, memory usage and task progress in real time, and supports log viewing.
• Instance Snapshot: Save the current environment status during shutdown and restore it at next startup to avoid repeated configuration.
• Team Sharing: Share computing power quota and storage space within the team, and manage the usage budget in a unified manner.

Model and version evolution

Version	Date	Key Changes
Latest version	—	Multiple GPU model support, daily/monthly billing, team sharing
Early Release	—	Single GPU type, hourly billing

The platform has experienced an expansion process from a single GPU type to multiple specifications of instances, and the billing method, environment provisioning and monitoring capabilities have continued to improve. The version record shall be subject to the official release notes.

Technical advantages

• Elastic Scheduling System: Based on containerization technology, computing resources are quickly allocated and recycled, and the user instance startup time is controlled at the level of minutes. Automatically queue and estimate waiting time when resources are tight.
• Data security isolation: Each user's computing instance runs in an independent environment, the storage and network between instances are completely isolated, and training data will not be leaked across users.
• Environment Image Management: Supports the creation and reuse of public images and custom images. Users can save the environment with installed dependencies and code as a private image and load it directly next time they boot up.

How to use

Entrance	How to use
Web side	Visit the official website with a browser → Register and recharge → Select the GPU instance type → Start the computing environment

Typical usage process: Visit the official website to register and recharge → Select the GPU type and billing method → Select the image (preconfigured or customized) → Start the instance → Perform model training or inference through the web terminal or SSH → Save the data and release the instance after completion. Billing is accurate to the hour, and billing stops after release.

Product Pricing

Package	Price	Contents
By hour	Pricing by GPU model	Billed after 1 hour, short-term testing and debugging
By day	Daily unit price (with discount)	24-hour continuous use scenario
Monthly subscription	Monthly unit price (significant discount)	Long-term training and continuous use

New users are provided with a certain amount of free experience after registration. The detailed price list is subject to the official real-time page.

Application scenarios

• Model Fine-tuning: Use open source base models to fine-tune on your own data and rent on demand to avoid the unnecessary expenditure of purchasing hardware for a single fine-tuning.
• Batch Inference: Perform model inference on large amounts of data, and can start multi-instance parallel processing in a short time.
• Scientific Research Experiments: For tasks such as ablation experiments and hyperparameter searches that require a large amount of parallel computing, the platform provides elastic expansion capabilities.
• Learning and Training: AI beginners can rent on demand during the deep learning learning process, which is more suitable for the computing power needs of the learning stage than self-purchased hardware.

Applicable people

• Independent AI Developer: Individuals who require GPU computing power for model development and are sensitive to on-demand billing and zero management costs.
• Researchers and graduate students: In deep learning experiments in academic research, the computing power demand is concentrated during the period, and the leasing model is better than the application time for the cluster.
• AI Start-up Team: Early teams that have not yet determined the level of computing power usage control expenditures through flexible leasing.
• AI Learner: Individuals who are learning deep learning technology and need a low-cost practical environment.
• Unsuitable Boundary: For enterprise-level scenarios that require long-term continuous running of large-scale distributed training, it is recommended to evaluate the comprehensive cost of long-term monthly subscription or self-built clusters.

Comparison of competing products

Comparison Dimensions	AI Computing Power	Vast.ai	Lambda Labs	RunPod
Core differences	Multiple and flexible billing methods	Decentralized market	Enterprise-level positioning	Billing per second
GPU model coverage	A100/V100/4090, etc.	Wide range (including consumer grade)	A100/H100	A100/4090, etc.
Billing Granularity	Hour/Day/Month	Hour/Day	Hour/Month	By Second
Free trial	✅ There is a quota for new users	❌ No free	❌ No free	✅ There is a free quota
Team Sharing	✅	❌	✅	✅
Pre-configured images	✅	✅	✅	✅

Summary and Outlook

AI Computing Power provides GPU computing power to individuals and small and medium-sized teams in a commercial form, lowering the threshold for obtaining computing power in AI development. Its core value lies in converting large fixed hardware investments into flexible, variable expenditures.

Advantages: Multiple billing methods flexibly match different usage patterns; team sharing function is suitable for group collaboration; new users have free experience to reduce trial costs.

Current limitations: GPU models and regional coverage are still being expanded; some high-end models may have queues during peak demand periods; user numbers and enterprise cases are not disclosed.

Risk Disclosure:

1. Resource competition risk: Computing power platforms generally face the problem of high-end GPUs being in short supply. Queues may occur during peak demand periods (such as the large model training season), affecting delivery time.
2. Price Fluctuation Risk: GPU computing power market pricing fluctuates greatly due to the relationship between supply and demand, and long-term projects may face price adjustments.
3. Risk of squeeze from competing products: Competing products such as Vast.ai (price competition), Lambda Labs (enterprise services), and RunPod (billing by the second) each have their own advantages in segmented scenarios, and the platform needs to differentiate and compete.

Follow-up observation points: More fine-grained billing (by minutes); latest Blackwell architecture GPU support progress; one-click deployment of inference services for common models.

It is recommended that users with computing power needs experience the startup speed and ease of use of the platform through free quota, and confirm the match before putting it into official use.

Related tools: crewai, langchain

Version Info

• Public beta version ：It is currently a publicly accessible version, and specific functions will be updated at a specific pace. （2026-07-14）
• earlier version ：An early trial version, the core direction is consistent with the current version. （2026-07-14）