HomeElectronics NewsStorage Technology Designed to Extend AI Memory

Storage Technology Designed to Extend AI Memory

A SSD uses flash memory to help AI systems access data and work with larger datasets without adding more high-bandwidth memory.

Kioxia Announces KIOXIA GP1 Series Super High IOPS SSDs for AI Applications
Kioxia Announces KIOXIA GP1 Series Super High IOPS SSDs for AI Applications

A new PCIe 6.0 NVMe SSD delivers up to 10 million random read IOPS at a 512-byte block size. It is designed for GPU direct access and targets AI infrastructure that needs higher storage performance and additional memory capacity.

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The KIOXIA GP1 Series is the first product in the KIOXIA GP Series of Super High IOPS SSDs. It uses KIOXIA XL-FLASH generation 2 flash memory and is designed to support flash-based memory extension for AI systems.

The SSD is designed for emerging AI storage architectures that use High Bandwidth Memory (HBM) together with a flash-memory-based tier. This allows AI systems to access larger datasets without adding more HBM, which can reduce memory costs and help improve GPU utilisation.

Compared with conventional TLC-based SSDs, the KIOXIA GP1 Series uses low-latency, high-performance XL-FLASH generation 2 memory to provide higher IOPS, finer-grained 512-byte data access, and lower power consumption per I/O.

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The architecture is designed to scale from the current 10 million random read IOPS to future generations targeting up to 100 million IOPS.

Key features

  • Designed to meet PCIe 6.0 and NVMe 2.2 specifications
  • Uses KIOXIA XL-FLASH generation 2 low-latency flash memory
  • Up to 10 million random read IOPS at a 512-byte block size
  • Available in E3.S and E1.S 9.5 mm and 15 mm form factors
  • Supports cold-plate liquid cooling in E3.S and E1.S 9.5 mm form factors
  • All form factors support conventional air-cooled environments
  • Up to 50 DWPD endurance

The KIOXIA GP Series technology was introduced earlier in 2026. The GP1 Series builds on this technology for higher IOPS and GPU-focused storage access.

Click here for the original announcement.

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Nidhi Agarwal
Nidhi Agarwal
Nidhi Agarwal is a Senior Technology Journalist at Electronics For You, specialising in embedded systems, development boards, and IoT cloud solutions. With a Master’s degree in Signal Processing, she combines strong technical knowledge with hands-on industry experience to deliver clear, insightful, and application-focused content. Nidhi began her career in engineering roles, working as a Product Engineer at Makerdemy, where she gained practical exposure to IoT systems, development platforms, and real-world implementation challenges. She has also worked as an IoT intern and robotics developer, building a solid foundation in hardware-software integration and emerging technologies. Before transitioning fully into technology journalism, she spent several years in academia as an Assistant Professor and Lecturer, teaching electronics and related subjects. This background reflects in her writing, which is structured, easy to understand, and highly educational for both students and professionals. At Electronics For You, Nidhi covers a wide range of topics including embedded development, cloud-connected devices, and next-generation electronics platforms. Her work focuses on simplifying complex technologies while maintaining technical accuracy, helping engineers, developers, and learners stay updated in a rapidly evolving ecosystem.

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