• Filecoin ≠ computing power itself (it is not a pure GPU computing power network like Bittensor/Render)

  • But AI computing power 100% relies on Filecoin (storing datasets, storing models, storing logs, storing weights, performing data validation)

  • Now Filecoin is becoming 'storage with computing power' where storage nodes can stack GPUs; wherever the data is, the computing power is.

Let's clarify below: how to use it, why to use it, and where we are now.

1. How AI computing power uses Filecoin (4 core scenarios)

1. Massive training datasets: low cost, high reliability, verifiable cold / warm storage AI large model training easily reaching tens of TB to several PB of data:

  • • Original materials, labeled sets, cleaned data, multi-version datasets

  • • Traditional clouds (AWS S3) are expensive, easy to delete, unprovable, and prone to censorship

    Filecoin advantages:

    • • Costs 30%-50% lower than centralized clouds

    • • Permanent data + provable existence + anti-tampering (cryptographic proof)

    • • FVM smart contracts: automatic renewals, data DAO, assetization of datasets

    • • Launching in 2026Filecoin Warm Storagesupports frequent AI reads

      Usage:

    • Upload all training sets, validation sets, and test sets to Filecoin

    • Computing networks (Render/Bittensor/io.net) directly pull data from Filecoin for training

2. AI model weights / Checkpoint: version management + anti-tampering + reproducibility

Large model files (.bin/.safetensors/ckpt):

  • • Large volume (7B/13B/70B parameters)

  • • Extremely high value, must be tamper-proof, reproducible, and traceable

  • • Training interruptions / restarts / fine-tuning rely on historical weights

Filecoin solves:

  • • Storage + hash verification: download to verify integrity

  • • On-chain records: who stored, when, what version, all training configurations are on-chain

  • • Suitable for: open-source models, academic reproduction, enterprise model asset management, compliance audits

3. Data preprocessing: perform 'light computing' nearby on storage nodes (computing close to data) the biggest pain point: PB-level data transported on the public network → bandwidth explosion, high latency, high costs.

Filecoin FVM solution:

  • • Allows local data cleaning, labeling verification, format conversion, and feature extraction on storage nodes

  • • No need to transfer data to remote GPUs, saving 90% bandwidth

  • • Belongs to the 'storage with light computing power' model, which is a key step in DePIN's 'computation-storage integration'

4. Complete closed loop: AI computing network ↔ Filecoin (mainstream architecture 2025–2026)

Currently, mainstream DePIN AI projects are deeply integrated with Filecoin:

  • • io.net: has connected to over 1500 Filecoin storage nodes' GPUs, forming an 'integrated storage and computing' network

  • • Bagel: GPU Restaking → storage nodes perform storage + GPU computing + privacy calculations simultaneously

  • • Aethir, Render, TensorOpera: use Filecoin to store model outputs and training data

  • • SingularityNET, Theoriq: metadata, Agent data stored on Filecoin

Typical process:

  1. • Dataset → Filecoin

  2. • Computing task scheduling → nearby Filecoin nodes with GPUs

  3. • Training / inference → intermediate results written back to Filecoin

  4. • Final model → permanent storage on Filecoin + on-chain proof

II. Why AI computing power must use Filecoin (core value)

1. Cost revolution

  • • Storage costs: about half lower than AWS/GCP

  • • Bandwidth costs: nearby computing + reduced cross-network transfers, saving a significant amount

  • • Suitable for AI such asextremely storage-intensive + bandwidthindustries

2. Data trustworthiness and compliance (strong demand in the AI industry)

  • • Verifiable: data has not been altered, truly exists, and is truly usable

  • • Anti-censorship, anti-single-point deletion

  • • Meet financial / medical / autonomous driving compliance audit requirements

3. Computation-storage integration (DePIN's ultimate form)

  • • Traditional cloud: storage is storage, computing is computing; cross-zone transfers are expensive and slow

  • • New paradigm of DePIN: storage nodes equipped with GPUs → data remains static, computing power moves

  • • Filecoin is the world's largest distributed storage network → naturally becomes the foundation for AI computation-storage integration

III. Positioning of Filecoin in the AI computing ecosystem (summary in one sentence)

  • • Bittensor / Render / io.net = CPU/GP for AI (computing layer)

  • • Filecoin = hard drive + memory + data bank + notary for AI (storage + data layer)

Relationship:

  • • Computing networks can operate without Filecoin → but data is expensive, untrustworthy, hard to manage, and hard to comply with

  • • Filecoin can operate without computing networks → but it's just a 'hard drive', not AI infrastructure

  • • The combination of both = a complete decentralized AI cloud (DePIN AI Cloud)

IV. Conclusion

AI computing power cannot directly 'use Filecoin as GPU', but AI computing power 100% needs Filecoin to store data, store models, validate, and reduce costs; and starting from 2025, Filecoin storage nodes are rapidly adding GPUs, becoming 'storage with computing power', in the era of AI computing power / large models, Filecoin is the most core and essential underlying support (storage foundation) and is quickly evolving into an integrated DePIN cloud of 'storage + nearby computing power'. It has become one of the most core infrastructures for AI DePIN.

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