From Hot Storage to Cold Memory: Decentralized Storage in the Storage Boom of the AI Era

Author: Jacob Zhao @ IOSG

Today, "the first domestic storage stock" Changxin Storage officially landed on the ChiNext board, igniting the scene with an astonishing surge of 500%. Although the storage sector as a whole is still affected by recent market corrections, AI storage continues to be crazily revalued by capital in the current wave of technological narrative. Meanwhile, decentralized storage in the Web3 domain has fallen into a long period of silence and loss. Why does the market performance differ so drastically for two entities both labeled as "storage"? The fundamental answer lies in the complete divergence of underlying value functions.

The revaluation of storage in the AI era is essentially a carnival about "hot data efficiency," serving the ultimate maximization of computing power utilization and commercial monetization; while decentralized storage adheres to the value proposition of "cold data trustworthiness," defending data fairness, anti-censorship, and the long-term memory of human civilization. The former is an efficiency system for hot data, while the latter is a trust system for cold data. The current capital market undoubtedly firmly stands on the side of "efficiency," but human civilization ultimately still needs an immutable memory foundation. The long-term value of trustworthy cold storage has never disappeared; it has merely been dormant in the dark side of the cycle, waiting to be repriced by the times.

Why Storage Has Re-emerged as the Focus of the AI Industry Chain

In the traditional IT era, storage was a "capacity business." CIOs focused on unit capacity costs, hard drive reliability, disaster recovery plans, archiving strategies, and equipment update cycles lasting 3 to 5 years. Storage was seen as an accessory that followed server procurement.

This round of storage boom is not a traditional cycle recovery but a revaluation of data mobility capabilities driven by AI. In the era of large models, storage logic has transformed from "capacity first" to "efficiency supreme," focusing on extreme metrics such as GPU feeding rates, Checkpoint writes, and extremely low latency for RAG. This marks a leap in storage value from "the final resting place of data" to "the high-speed passage for data entering computation."

The evolution of resource bottlenecks in AI infrastructure is essentially a battle to complete the "barrel effect." The true utilization rate of computing power is not a linear addition of single assets but a harsh multiplicative effect: true utilization rate = GPU × HBM × DRAM × SSD × network × file system; any shortcoming in one link will lead to the collapse of overall computing power utilization. In the AI era, storage has transformed for the first time from a "cost center" to an "efficiency engine." This is the fundamental logic behind the repricing of storage.

AI Storage Architecture Overview: From HBM Bandwidth Organs to Data Lake Foundations

AI storage is by no means a mere accumulation of single hardware but a complex system of tightly coupled, layered scheduling. In this system, industrial value and capital focus are highly concentrated on HBM, enterprise-grade SSDs, SSD controllers, NVMe/CXL protocols, and high-performance storage systems. To clearly dissect its value flow, we divide the AI storage architecture into four core levels from top to bottom:

• Compute Proximal Memory Layer (Bandwidth Core): Dominated by HBM, supplemented by DRAM and CXL memory pooling technology. This layer directly interfaces with GPU/CPU packaging or buses, aiming to break the "memory wall," and is the first checkpoint determining whether computing power can be fully unleashed.

• High-Speed Persistent Storage Layer (IO Hub): The core logic is enterprise-grade SSD = NAND chips + SSD controllers + NVMe/PCIe data pathways. This layer handles high-frequency Checkpoint writes, massive training set loading, and RAG hot data caching, representing the clearest persistent storage increment for AI data centers.

• Low-Cost Large Capacity Storage Layer (Capacity Foundation): Composed of HDDs, cold storage, and data lake archiving systems. In the face of exponentially expanding multimodal raw data, historical logs, and compliance backups, this layer still provides an irreplaceable TCO (Total Cost of Ownership) advantage.

• AI Storage Systems and Data Software (Scheduling Brain): Includes high-performance parallel file systems, distributed object storage, vector databases, and RAG data governance layers. What AI truly consumes is not bare hardware but the data availability that has been efficiently organized, indexed, and authorized by the software stack.

As an ecological extension, decentralized storage does not directly engage in the millisecond-level competition of AI hot data but instead anchors public dataset notarization, AI training data provenance, and long-term cold memory archiving, establishing its unique ecological niche as a "trustworthy cold layer." HBM: The "Bandwidth Organ" Closest to Computing Power in the AI Storage Chain High Bandwidth Memory (HBM) is not traditional storage but a high-bandwidth memory layer proximal to the GPU. Its core mission is not to store data but to continuously "feed" data to computing power at extremely high bandwidth. HBM is the closest and most deterministic link in the AI storage chain to computing power, directly determining whether the GPU can be "fed," and is currently the core supply chain bottleneck.

The core architecture of HBM is "3D DRAM stacking + 2.5D advanced packaging": through TSV vertical stacking and CoWoS heterogeneous integration, it achieves extreme compression of storage-computation distance, resulting in a generational leap in bandwidth. Its industrial barriers are not just DRAM design but also involve DRAM manufacturing processes, TSV, ultra-thin stacking, packaging, heat dissipation, testing, and customer certification as a system engineering. Any defect in yield at any link can lead to the scrapping of the entire HBM stack.

Currently, only SK Hynix, Samsung, and Micron can stably mass-produce, establishing a triple moat of top-tier DRAM manufacturing processes, packaging capabilities, and NVIDIA/AMD customer certifications. DRAM and CXL: System Memory Foundation and Memory Pooling Engine HBM addresses the extreme bandwidth near the GPU, DRAM solidifies the server system memory foundation, and CXL attempts to break physical boundaries, reconstructing the organization of memory resources in data centers.

• DRAM: Primarily carries CPU-side cache, data preprocessing, intermediate state storage, and system operation, serving as the most basic system memory layer for servers. The global DRAM market is highly concentrated among the three giants SK Hynix, Samsung, and Micron; Changxin Storage (CXMT) is a core variable in China's DRAM domestic substitution.

• CXL (Compute Express Link): A next-generation cache coherence interconnect protocol for data centers, aimed at breaking the limitations of traditional DIMM slots, local memory capacity, and server memory resource islands, promoting the evolution of memory architecture towards expansion, pooling, and sharing. Currently, CXL is still in the early stages of transitioning from platform support to large-scale deployment, with high mid-to-long-term architectural value; core companies include Astera Labs and Lanqi Technology.

Enterprise-grade SSD: The Data Hub Built from NAND, Controllers, and NVMe Enterprise-grade SSDs are the core high-throughput persistent increments in AI data centers, continuously "feeding" data to GPUs with extremely high throughput, extremely low latency, and stable QoS, spanning the entire lifecycle of training data loading, Checkpoint writing, RAG retrieval, inference caching, and log backflow.

In the AI storage architecture, SSDs are not isolated hardware but a highly coupled system, distilled into an industry formula: enterprise-grade SSD = NAND chips + SSD controllers + NVMe/PCIe data pathways. The three layers represent independent links in the industrial chain:

• NAND Chips (Raw Material Layer): Determine storage density and unit cost, while controllers manage performance release and lifespan. Representative companies include Samsung, SK Hynix (Solidigm), Micron, Kioxia, Western Digital, and Yangtze Memory Technologies.

• SSD Controllers (Performance Empowerment Layer): Determine performance release, data error correction, QoS stability, and wear leveling. Representative companies include Phison (群联), Silicon Motion (慧荣), Marvell, and Maxio (联芸).

• NVMe/PCIe (Data Pathway Layer): Determine the transmission efficiency of data from storage to computation. Combined with GPUDirect Storage technology, it reduces CPU memory bounce buffers and CPU involvement, significantly alleviating I/O bottlenecks. Representative companies include Broadcom, Marvell, and Astera Labs.

HDD / Cold Storage / Archiving: The Low-Cost Foundation of AI Data Lakes AI will not eliminate HDDs. With the demand for multimodal large models for video and image data, as well as the exponential growth of enterprise compliance logs and historical datasets, the demand for low-cost cold data storage is surging. In the AI storage architecture, SSDs and HDDs collaborate in a layered manner based on business value: SSDs handle hot data and high throughput, while HDDs manage low costs and long-term preservation. Representative companies include Seagate, Western Digital, and Toshiba. AI Storage Software Stack: The Scheduling Hub of Data Availability What AI truly consumes is never bare disks but "data services" that have been meticulously organized by the software stack. This architecture transforms underlying hardware into knowledge assets that can be directly invoked by AI, specifically divided into four layers:

• High-Performance Storage Systems (Supply Systems): Focused on concurrent throughput and low latency, solving the "data hunger" problem of GPU clusters through parallel file systems, ensuring rapid flow for training and inference. Representative companies include VAST Data, WEKA, and Pure Storage.

• Object Storage (Raw Data Lake): Centered on Object, Key, and Metadata management, carrying massive amounts of unstructured data. It does not pursue extreme low latency but builds a capacity foundation with low cost and cloud-native characteristics. Representative companies include AWS S3.

• Vector Databases (Semantic Indexing Layer): Responsible for storing, indexing, and retrieving vectors generated by embedding models, allowing AI to accurately locate relevant content from vast knowledge. Representative companies include Pinecone and Milvus.

• RAG Data Layer (Knowledge Invocation Layer): Going beyond simple retrieval, encompassing data slicing, cleaning, permission control, and citation provenance, ensuring that enterprise data can be safely, accurately, and traceably invoked by large models. Representative companies include Databricks.

From AI Hot Storage to Decentralized Cold Memory: Efficiency Maximization vs. Trust Maximization

AI Storage is an extremely efficiency-driven system, with its value function focused on maximizing computational output. HBM bandwidth determines whether the GPU can be fed, SSD throughput determines the read/write efficiency of datasets and Checkpoints, and low latency is crucial for the real-time experience of RAG and inference. These metrics ultimately converge into GPU utilization and unit Token costs, directly determining the commercial profitability of AI applications. The ultimate goal of AI storage is not preservation but acceleration, serving productivity.

In contrast, the value function of decentralized storage is entirely different. It questions whether data will still exist in ten years, whether it can be tampered with, and whether it can resist single-point censorship. Through cryptographic proof and distributed networks, it constructs an open-access and permanently preserved public data foundation. Its ultimate goal is to defend the absolute truth of data and sovereign independence, serving fairness, anti-censorship needs, and civilizational memory. AI storage provides fuel for future productivity as "hot storage," while decentralized storage preserves irremovable historical records for human civilization as "cold memory." The former serves efficiency, pursuing extreme speed; the latter serves trust, defending silent memory. The former determines how fast models run, while the latter determines whether memories will be erased. Currently, the market rewards productivity efficiency without reservation, placing AI storage at the forefront, while decentralized storage seems to be experiencing valuation collapse and narrative depletion.

The Vision and Reality of Decentralized Storage

There are many decentralized storage projects, but based on industry mindset and ecological sedimentation, the core representatives remain Filecoin and Arweave. Although both belong to "decentralized storage," their underlying architectural philosophies are nearly two completely different paths— the former approaches AWS's elasticity through market contracts, while the latter approaches the eternity of libraries through one-time social contracts.

• Filecoin: Constructs the most complete verifiable economic system through PoRep and PoSt. It should not continue to compete with AWS on consumer-grade cloud storage but should shift towards AI data provenance, public dataset hosting, and compliance archiving, providing verifiable chains for model auditing and copyright proof. The necessary path is to package as an S3-compatible API and support fiat payments, upgrading from a "cheap storage market" to "verifiable computing infrastructure."

• Arweave: Through the narrative of "one-time payment, permanent storage," it forces miners to preserve and quickly access as much, especially scarce, historical data as possible through Blockweave and SPoRA mechanisms. Its best position is as a foundation for human public memory—preserving human rights records, war crime evidence, cultural classics, and archiving legal and financial history, providing AI agents with permanently accessible long-term memory. The value of Arweave lies not in speed but in its capacity to carry civilizational memory across cycles.

The dilemma faced by decentralized storage projects like Filecoin and Arweave is not due to incorrect value propositions but rather a long-term mismatch between productization, retrieval experience, real demand, and Token incentives. This reveals a significant gap from geek ideals to mainstream commercial applications:

• Supply-Demand Incentive Mismatch: Early networks represented by Filecoin rapidly expanded through Tokens but did not build a sufficiently strong paid demand side, resulting in massive capacity but insufficient utilization and payment conversion. They rewarded "I can store" rather than "I need to store."

• Lack of Enterprise Service Capability: AWS's barrier is not hard drives but a "data operating system" composed of APIs, SLAs, permission management, compliance auditing, and technical support. Enterprises purchase "peace of mind," not experimental infrastructure that requires them to handle key and node selection.

• Retrieval Experience Shortcomings: "Storing in" does not equal "stably and with low latency retrieving out." Node dispersion, complex topology, and lack of unified SLA make it difficult to support AI hot data workflows, making it more suitable for trustworthy cold archiving and data provenance.

• Insufficient Privacy Compliance: Private enterprise data cannot simply be written into a public permanent network; the right to delete and permanent immutability are inherently in conflict. Decentralized storage is more suitable for public data and long-term archives rather than indiscriminately accommodating core private data.

• Token Economy Amplifying Cycles: The financialization during bull markets obscures insufficient demand, while the decline in miner ROI during bear markets exposes commercialization shortcomings. Tokens can cold-start supply but cannot automatically create demand and sustainable revenue.

Other decentralized storage projects tend to focus on specific ecosystems or niche tracks: Storj/Sia's cross-cycle industry mindset and Web3 narrative influence are weaker than those of Filecoin/Arweave; BNB Greenfield/Walrus are tied to specific public chain ecosystems like BNB or SUI; Celestia/EigenDA belong to the data availability (DA) layer, serving Rollup transaction confirmations rather than long-term archiving; projects like 0G, which mix AI/DA narratives, attempt to integrate storage, data availability, computation, and AI agent settlement into a set of AI-native modular infrastructure, but their real demand, developer adoption, and commercialization closed loops still need verification.

Future Opportunities for Decentralized Storage: The Long-Term Pendulum of Efficiency and Trust

During the explosive period of technological dividends, capital frantically chases efficiency, with assets like GPUs and HBM assigned extremely high premiums, while decentralized storage advocating "trust and fairness" is naturally marginalized. However, the pendulum of history will not remain forever on the efficiency side. Events such as unreasonable bans and content deletions by super platforms, the outbreak of AI copyright lawsuits forcing data source proof, data sovereignty disputes triggered by geopolitical conflicts, the disappearance of public archives due to data monopolies, and regulatory pressures on compliance of model training data could all brew a repricing of "trustworthy storage," and decentralized storage's future opportunities may still reflect unique value in the following directions:

• AI Data Provenance: Combining cryptographic proof to construct "data lineage proof" to address regulatory and auditing pressures.

• Public Datasets and Civilizational Archives: Anchoring censored archives and cultural heritage, building irreplaceable and undeletable memories.

• Trustworthy Archiving and Compliance Notarization: Achieving trustworthy self-proof through Hash notarization, providing high-level digital notarization.

• Integration of ZK/TEE/DID Technologies: Resolving privacy tensions, upgrading from a single "storage protocol" to "trustworthy data infrastructure."

• Invisible Product Routes: Providing S3-compatible APIs and fiat billing, allowing users to directly purchase "trustworthy archiving" services.

AI storage and decentralized storage serve different purposes: one pursues extreme efficiency, providing fuel for our future; the other defends silent memory, safeguarding our right to look back at the past. The current market rewards efficiency without reservation, making decentralized storage seem silent or even collapsing; however, as the AI era further amplifies data monopolies, copyright disputes, and the fragility of historical memory, decentralized storage may welcome a repricing of value in the form of a "trustworthy cold layer." Memories that cannot be easily erased by platforms, companies, or any single power may transform from romantic idealism and marginal beliefs into necessary infrastructure.