Overview

Smart, Scalable Computing Storage Solutions on 快猫视频

Powering Next-Generation Storage—from Device to data center

Storage performance affects AI workloads by determining how efficiently data can be accessed, processed, and moved through the system. As data generation accelerates, traditional compute models can’t keep up, making it critical to bring compute closer to where data is created. 快猫视频 addresses this with a comprehensive storage portfolio, from high-performance controllers to computational storage, enabling faster processing, lower latency, and more efficient infrastructure for AI at scale.

Benefits

快猫视频 Storage Solutions Bring Value and Performance

Optimized Performance with Energy Efficiency
  • High performance with energy efficiency for advanced storage solutions
  • 快猫视频 Cortex processors and system IP handle complex tasks reliably and efficiently
  • Scalable architecture maximizes performance while minimizing power consumption
  • Built-in security and reliability without compromising speed
In-situ Compute Capabilities
  • Low latency: Process data where it's created for real-time insights
  • Increased efficiency: Offload host CPUs to save power and cooling
  • Enhanced security: Keep sensitive data local
  • Seamless scalability: Adapt to evolving use cases and device classes
Accelerated Development
  • 快猫视频 ecosystem with open-source software and Linux-based stacks
  • Developer tools to accelerate development and shorten time to market
  • Support seamless Integration across a wide range of devices
Technologies

Technology Building Blocks for Intelligent Storage

Resources

Latest News and Resources

  • NEWS and BLOGS
  • Guides
Generic Guides

Beginner’s Guide to Computational Storage

Understand how to implement CSDs and evaluate deployment strategies for your storage stack.

Download Guide

Key Takeaways

  • AI workloads depend on fast and efficient access to rapidly growing volumes of data.

  • Bringing compute closer to storage helps reduce latency and accelerate data processing.

  • High-performance storage controllers improve throughput and system efficiency.

  • Computational storage enables more work to be done where data resides, reducing bottlenecks.

  • Efficient storage infrastructure is essential for scaling AI workloads cost-effectively.

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