威九国际

Product Overview
AI File Storage

AI File Storage (AFS) is a scalable parallel file storage system launched by SenseCore, which is optimized for GPU-based high-performance computing scenarios and characterized by providing sub-millisecond-level data access, meeting high-IOPS and high-throughput data read and write requests, and applying to high-performance computing scenarios such as AI training, autonomous driving, and video rendering.

Product Superiority
In-depth optimization for AI application requirements, high reliability, high performance, and low latency.
  • 01In-depth optimization for AI application scenarios
  • 02High reliability
  • 03Elastic scalability
In-depth optimization for AI application scenarios
01In-depth optimization for AI application scenarios

Realize in-depth optimization for AI application scenarios by using a distributed parallel architecture and supporting a throughput of tens of GB, sub-millisecond-level latency, and millions of IOPS

High reliability
02High reliability

Reach up to 99.9% availability of architecture design and stable performance for tens of billions of small files without attenuation

Elastic scalability
03Elastic scalability

Support online elastic scalability to meet the linear growth of capacity requirements and performance

In-depth optimization for AI application scenarios
01
In-depth optimization for AI application scenarios

Realize in-depth optimization for AI application scenarios by using a distributed parallel architecture and supporting a throughput of tens of GB, sub-millisecond-level latency, and millions of IOPS

High reliability
02
High reliability

Reach up to 99.9% availability of architecture design and stable performance for tens of billions of small files without attenuation

Elastic scalability
03
Elastic scalability

Support online elastic scalability to meet the linear growth of capacity requirements and performance

01
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03
Product Features
Rich storage functions for AI applications.
  • High-performance AI computing file storage
    High-performance AI computing file storage

    Support the POSIX file protocol, and efficiently execute high-performance AI computing programs without the need of adaptation and optimization;

  • Close integration with SenseCore products
    Close integration with SenseCore products

    Integrate with bare metal, container, and cloud lab computing platforms to facilitate support for the AFS file system; and support SenseCore container services and provide a high I/O capability for container services through the CSI interface;

  • High throughput and low latency
    High throughput and low latency

    Give full play to the advantages of concurrent access in a parallel I/O architecture, and maintain ultra-low latency of sub-milliseconds when the cluster achieves high throughput through horizontal scaling.

Application Scenarios
Applicable to different high-performance AI application scenarios.
  • 01AI training
  • 02Autonomous driving
  • 03High-performance computing storage
AI training
AI training
The main data type for AI model training is a large number of small files, which have low latency requirements.

AFS is deeply integrated with AI application scenarios, supports access to massive small files, and improves AI training performance.

It is connected with various computing clusters to provide ultra-high throughput and ultra-high IOPS capabilities.


Autonomous driving
Autonomous driving
In the autonomous driving scenario, the daily data volume of each test vehicle is about 1TB. The data needs to be analyzed quickly, and the storage cost of massive data needs to be controlled.

The high performance and elastic scalability of AFS strongly support the large capacity and high performance requirements brought about by customers’ business growth.

AFS is used in conjunction with AOSS. The data to be trained is imported from AOSS to AFS, and model training is performed in AFS. After the training is completed, the data is archived to AOSS for long-term storage.


High-performance computing storage
High-performance computing storage
It is centered on AFS and seamlessly integrated with bare metal, cloud containers, and cloud lab computing products to provide high-performance computing storage solutions for your diverse businesses.

AFS also provides high throughput and high IOPS to improve computing efficiency and reduce the overall business cost.

The architecture supports elastic scalability to achieve linear growth in capacity and performance.

01AI training
02Autonomous driving
03High-performance computing storage
AI training
AI training
The main data type for AI model training is a large number of small files, which have low latency requirements.

AFS is deeply integrated with AI application scenarios, supports access to massive small files, and improves AI training performance.

It is connected with various computing clusters to provide ultra-high throughput and ultra-high IOPS capabilities.


Autonomous driving
Autonomous driving
In the autonomous driving scenario, the daily data volume of each test vehicle is about 1TB. The data needs to be analyzed quickly, and the storage cost of massive data needs to be controlled.

The high performance and elastic scalability of AFS strongly support the large capacity and high performance requirements brought about by customers’ business growth.

AFS is used in conjunction with AOSS. The data to be trained is imported from AOSS to AFS, and model training is performed in AFS. After the training is completed, the data is archived to AOSS for long-term storage.


High-performance computing storage
High-performance computing storage
It is centered on AFS and seamlessly integrated with bare metal, cloud containers, and cloud lab computing products to provide high-performance computing storage solutions for your diverse businesses.

AFS also provides high throughput and high IOPS to improve computing efficiency and reduce the overall business cost.

The architecture supports elastic scalability to achieve linear growth in capacity and performance.

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