To address the surging KV cache demand driven by the development of agentic AI, cloud service providers are shifting their procurement toward high-performance QLC SSDs. By leveraging their high density and cost efficiency, combined with 'write-once, read-many' operating patterns and advanced attention compression techniques, QLC SSDs effectively overcome capacity bottlenecks while reducing hardware wear, making them the mainstream configuration for advanced storage units.

Key Highlights

• Demand Driver: Expanding context windows in agentic AI cause KV cache demand to explode, prompting cloud providers to shift from traditional setups to QLC SSD PODs.
• Cost & Density: QLC delivers higher density at lower cost. Its limited endurance and latency are mitigated by AI's write-once, read-many access pattern.
• Algorithmic Efficiency: Advanced attention compression techniques drastically lower write frequency and storage footprints, reducing drive wear.
• Market Adoption: Major tech firms are rapidly deploying QLC SSDs. As memory demands scale, broader market transition remains inevitable.

Table of Contents

1. CSPs’ Changes to Purchase Sentiment on SSD POD: Key Factors behind Emergence of QLC SSD
2. Most CSPs’ Previous Procurement Focused on TLC SSD + HDD for SSD POD Configuration
3. Demand for KV Cache at Agentic AI Phase
   • Schematics of Training and Inference (Agentic AI) Flow
   • Comparison of Capacity Required by KV Cache and HBM Capacity of GB300 NVL72
   • Architecture of AI Inference
4. Advantages of QLC in Agentic AI Applications
   • Structure of 3D NAND
   • Comparison of Enterprise SSD Technology
   • Comparison of Non-Agentic AI and Agentic AI Application Flows
   • Comparison of Read/Write Tokens for KV Cache of Claude Code
5. QLC Applications under Improved Algorithms
   • DeepSeek’s HCA Architecture
6. Market Dynamics
   • Release Timeline of Mainstream AI Agents
   • Conversion Process of TurboQuant Polar Coordinates

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