Everyone is probably familiar with cloud computing. Over the past decade, it can be said that cloud computing has supported the global wave of digitalization. And edge computing — some people see it as a technological advancement of cloud computing, but the reality may not be that simple. With the boost of Web3 and AI, edge computing is redefining how computing resources are distributed, and it is also reshaping the infrastructure landscape of every online industry in the future.

From traditional internet services and AI model execution, to Web3, public chain scaling, and crypto trading scenarios, edge computing is everywhere. If we have to express it as a single pathway, then I believe it is this:AI computing → real-time collaboration → Web3 decentralization → global computing power network.

This is obviously not a simple technical upgrade, but a paradigm shift. Perhaps in the next 10 years, edge computing will gradually become an unavoidable core technological trend.

If we view “cloud computing” as a giant city of servers, then edge computing is to “break apart” these servers and deploy them to every corner of the city: beside routers, next to base stations, near data centers, around small nodes, on enterprise premises, and even inside personal devices.

Edge Computing = placing computing power closer to users.

The traditional cloud computing logic is: all data is uploaded to a remote data center, processed by centralized servers, and then the results are sent back to the endpoint. The problems are:

• Latency is too high

• Traffic and bandwidth pressure is enormous

• Real-time data response is not fast enough

Facing the demands of AI and Web3, traditional cloud computing simply cannot hold up, while edge computing works like this: → data is processed locally (or at nearby nodes) directly, without having to return to the remote cloud.

Therefore, it brings three core advantages:

1. Ultra-low Latency

AI inference, autonomous driving, robotics, and real-time trading systems require millisecond-level response, which cloud computing simply cannot achieve.

2. Data stays local, improving privacy and security

Many sensitive data types do not need to be sent back to the cloud to be processed, such as Web3 wallet signing, cryptographic computation, and local AI computation.

3. Higher computing efficiency, lower cost

Processing data at the source → significantly reduces bandwidth costs.

If in the past edge computing mostly stayed in the stage of academic and industrial exploration, then in 2024–2025, it has officially entered the phase of large-scale deployment. The three forces driving this explosion are very clear: AI, Web3, and a surge in global computing power demand.

ChatGPT, Sora, Midjourney, intelligent Agents, large-model inference…… all of them require:

• faster responses

• local deployment capabilities

• reduced cloud computing costs

So a large number of enterprises have begun looking for a “cloud + edge” hybrid architecture, making AI computing distributed.

Smartphones, IoT, and smart hardware themselves are becoming more powerful, making “edge nodes” everywhere. Every one of your devices may become an edge node in the future.

• decentralization

• point-to-point

• a more open network architecture

Edge computing is naturally aligned with the philosophy of Web3.

Autonomous driving, AI robots, real-time AI generation (video / audio / digital humans), high-frequency trading, gaming, AR, the metaverse — these scenarios cannot function without the low-latency capabilities of edge computing.

Many people misunderstand and think edge computing is a “next-generation replacement for cloud computing.” In fact, it is not. The correct relationship should be understood as:

• Cloud computing is responsible for “large-model training + large-scale centralized computing power”

• Edge computing is responsible for “inference, real-time response, low latency”

The two form a “cloud + edge” hybrid model:

The future network will not be an “era of cloud,” nor an “era of edge,” but an era of hybrid computing power with cloud + edge.

AI is rapidly shifting from the previous single-pole model of “computing power relying on cloud centers” to a multi-layer architecture of “cloud + edge + device parallelism.” And the reason behind this is not driven by trends, but by an inevitable evolution jointly driven by technical bottlenecks, economic costs, data security, and user experience.

Over the past decade, AI’s core competition points were model size, parameter count, and training capability, so almost all technology companies rushed to build massive data centers to train bigger models. But today, what truly determines industrial adoption is not “training,” but “inference,” meaning the real-time computing capability when tens of millions of users use AI simultaneously.

As the user scale grows from millions to billions, cloud inference is not only extremely expensive, but latency also continues to rise. When tens of billions of devices generate requests at the same time, the cloud cannot possibly withstand such massive concurrent pressure no matter how it scales, which forces inference to sink down to edge nodes and terminal devices.

Whether it is Apple’s A17 Pro / M3 chips, Samsung Exynos, or Qualcomm’s latest Snapdragon series, they all treat NPU (Neural Processing Unit) performance as a core selling point, and even propose that “your phone is your local large model running center.”

This trend means that in the future, all kinds of smart terminals — phones, earbuds, watches, cars, cameras, IoT devices — will have the computing power to run AI inference independently. They can execute tasks such as speech recognition, image analysis, behavior prediction, interaction understanding locally, without uploading data to the cloud for processing. This not only makes AI faster (millisecond-level response), more energy-efficient (no long-duration network transmission), more stable (can run offline), and more importantly, it is completely changing the operating model of AI from “cloud-computing centralization” to “on-device intelligent distribution.”

The data processed by AI is extremely sensitive, including voice, face, fingerprints, location behavior, daily conversations, health information, app habits, and more. If all of this data is uploaded to the cloud, even with encrypted transmission, there is still potential leakage risk. Edge computing allows computation to be completed locally, and sensitive data does not need to leave the user’s device — only necessary inference results are returned, greatly reducing the probability of privacy exposure.

Especially in high-privacy fields such as healthcare, finance, social networking, and vehicle-to-everything connectivity, on-device AI is no longer an option, but a necessary condition for compliance and security.

The cost of cloud inference is often several times the training cost. As AI usage continues to grow, cloud inference computing power becomes a “bottomless pit.” By pushing inference down to devices, enterprises can “spread” inference costs across billions of terminal hardware devices globally, greatly reducing server pressure. Industry experts even predict: in the future, 80% of AI inference will occur on terminal devices, while the cloud will mainly be responsible for training and model updates.

The value of edge computing is not merely “faster, lower latency.” It is becoming the key driving force behind the next wave of infrastructure revolution in the crypto industry. Whether it is public chain performance bottlenecks, privacy protection, AI on-chain, metaverse rendering, or latency competition in high-frequency trading, edge nodes are taking on work that previously had to be handled by cloud computing or super servers, bringing a decentralized and high-efficiency computing power structure to the blockchain ecosystem.

The following six directions are the true core areas where edge computing will profoundly reshape Web3.

Today’s public chain nodes are distributed globally, but their quality varies greatly — high latency, unstable bandwidth, RPC requests timing out easily — so many users feel that on-chain interactions are “laggy” during peak times. The emergence of edge nodes allows regional local nodes to take on the role of the access layer, shortening data traffic that originally required cross-continent access into a local transmission range of just a few milliseconds.

This means:

• RPC request responses are faster

• Wallet signing experience is smoother

• DEX depth queries and pricing are more timely

• Validator node synchronization latency is lower

• More complex on-chain interactions can be executed in real time

For scenarios like on-chain trading, GameFi operations, and real-time oracle reads, this is not just a performance improvement — it is an experience upgrade from “usable” to “silky smooth.”

Filecoin, Arweave, IO.Net, Render Network, and other DePIN projects are becoming the fastest-growing Web3 track globally, and the core of their success lies in: edge computing enables every device to become a distributed storage or computing node.

In the past, only large mining farms or professional nodes could participate. Now:

• NAS

• spare hard drives

• home broadband

• even routers and TV boxes

can all connect to decentralized networks and contribute storage and bandwidth. This real network composed of millions of global nodes will be more cost-advantageous and resilient than any centralized CDN or cloud storage, providing long-term reliable infrastructure for AI data lakes, on-chain data, and NFT assets.

For GameFi and the metaverse to truly achieve real-time interaction and multi-user dynamic rendering, relying solely on the cloud is impossible. Latency, bandwidth, and rendering load will cause the experience to collapse instantly. But with the addition of edge nodes, rendering can be completed at the node closest to the player, reducing latency by 80%.

Acceleration brought by edge rendering includes:

• real-time animation rendering for Web3 games

• regional loading of metaverse scenes

• reduced motion latency in VR/AR interactions

• complex graphics can be inferred locally

Future large-scale GameFi titles are very likely to adopt a hybrid architecture of “local rendering + on-chain settlement,” and edge computing is the core engine of this structure.

The trend in 2025 is already very clear: AI is integrating with Web3, and the key to integration is local inference + decentralized AI networks. Edge computing can achieve:

• user data is inferred locally, not uploaded to the cloud

• AI assistants are bound to DID identities

• decentralized hosting of on-chain AI models

• globally distributed collaboration of AI computing nodes

• Web3 + AI applications run under privacy protection

This means that in the future, you can have an AI assistant running on your phone. It will not hand your data to any company, but provide personalized services for you based on your on-chain identity — super private, super secure.

Crypto HFT bots, arbitrage systems, and liquidity market makers are, just like traditional finance, entering “millisecond-level competition.” The lower the latency, the higher the profit. Edge nodes can:

• route trading requests in advance to the nearest data center

• shorten latency for DEX quote refresh and oracle update

• increase arbitrage success rate and MEV front-running success rate

• reduce transaction failures caused by network congestion

If exchanges (such as SuperEx) deploy edge architecture in the future, user trading latency can be reduced to an experience several times faster than traditional cloud architecture.

DID will become an important infrastructure of the Web3 ecosystem in the future, but users’ identity data is often extremely private, including:

• biometric information

• behavior patterns

• wallet preferences

• personal on-chain footprints

If all of this data is uploaded to the chain or centralized servers, the risk is extremely high. Edge computing enables DID to achieve:

• all sensitive information is processed locally

• only zero-knowledge proofs are written on-chain

• no raw data is exposed

• can achieve fully private personalized services together with AI

This will be a key breakthrough point combining privacy computing and blockchain, and is regarded as one of the most valuable directions in the future crypto industry.

AI, Web3, and IoT will all rely on it. In the future, there are three trends most worth paying attention to:

In the future, every device may be assetized and become a node that can mine and earn rewards, which will promote:

• more democratic allocation of computing power

• more secure Web3 networks

• users become part of the network, rather than passive consumers

AI edge inference + Web3 incentive models will become the core driving force for application deployment. Your phone will:

• do AI inference

• do cryptographic signing

• participate in node networks

• earn rewards

A truly decentralized world starts from here.

All applications with strong real-time requirements will move onto edge nodes:

• trading

• cross-chain communication

• real-time oracles

• Layer2 DA modules

• wallet cryptographic computation

This will make the entire Web3 faster and lower cost.

• Cloud computing created the golden decade of Web2;

• Edge computing will create the next golden decade of AI + Web3.

It will become:

• the inference engine of AI

• the low-latency network of Web3

• the computing power foundation of DePIN

• the underlying architecture of global trading systems

• the “main artery” of the future digital economy

Edge computing is not only a technological trend, but also a key direction for future global deployment, Web3 decentralized community building, and trading infrastructure upgrades. Future users will not realize the existence of edge computing, but they will feel:

• faster trading

• safer data

• smarter AI

• freer Web3

• stronger network performance

This is the true value of edge computing.

Everyone is probably familiar with cloud computing. Over the past decade, it can be said that cloud computing has supported the global wave of digitalization. And edge computing — some people see it as a technological advancement of cloud computing, but the reality may not be that simple. With the boost of Web3 and AI, edge computing is redefining how computing resources are distributed, and it is also reshaping the infrastructure landscape of every online industry in the future.

From traditional internet services and AI model execution, to Web3, public chain scaling, and crypto trading scenarios, edge computing is everywhere. If we have to express it as a single pathway, then I believe it is this:AI computing → real-time collaboration → Web3 decentralization → global computing power network.

This is obviously not a simple technical upgrade, but a paradigm shift. Perhaps in the next 10 years, edge computing will gradually become an unavoidable core technological trend.

If we view “cloud computing” as a giant city of servers, then edge computing is to “break apart” these servers and deploy them to every corner of the city: beside routers, next to base stations, near data centers, around small nodes, on enterprise premises, and even inside personal devices.

Edge Computing = placing computing power closer to users.

The traditional cloud computing logic is: all data is uploaded to a remote data center, processed by centralized servers, and then the results are sent back to the endpoint. The problems are:

• Latency is too high

• Traffic and bandwidth pressure is enormous

• Real-time data response is not fast enough

Facing the demands of AI and Web3, traditional cloud computing simply cannot hold up, while edge computing works like this: → data is processed locally (or at nearby nodes) directly, without having to return to the remote cloud.

Therefore, it brings three core advantages:

1. Ultra-low Latency

AI inference, autonomous driving, robotics, and real-time trading systems require millisecond-level response, which cloud computing simply cannot achieve.

2. Data stays local, improving privacy and security

Many sensitive data types do not need to be sent back to the cloud to be processed, such as Web3 wallet signing, cryptographic computation, and local AI computation.

3. Higher computing efficiency, lower cost

Processing data at the source → significantly reduces bandwidth costs.

If in the past edge computing mostly stayed in the stage of academic and industrial exploration, then in 2024–2025, it has officially entered the phase of large-scale deployment. The three forces driving this explosion are very clear: AI, Web3, and a surge in global computing power demand.

ChatGPT, Sora, Midjourney, intelligent Agents, large-model inference…… all of them require:

• faster responses

• local deployment capabilities

• reduced cloud computing costs

So a large number of enterprises have begun looking for a “cloud + edge” hybrid architecture, making AI computing distributed.

Smartphones, IoT, and smart hardware themselves are becoming more powerful, making “edge nodes” everywhere. Every one of your devices may become an edge node in the future.

• decentralization

• point-to-point

• a more open network architecture

Edge computing is naturally aligned with the philosophy of Web3.

Autonomous driving, AI robots, real-time AI generation (video / audio / digital humans), high-frequency trading, gaming, AR, the metaverse — these scenarios cannot function without the low-latency capabilities of edge computing.

Many people misunderstand and think edge computing is a “next-generation replacement for cloud computing.” In fact, it is not. The correct relationship should be understood as:

• Cloud computing is responsible for “large-model training + large-scale centralized computing power”

• Edge computing is responsible for “inference, real-time response, low latency”

The two form a “cloud + edge” hybrid model:

The future network will not be an “era of cloud,” nor an “era of edge,” but an era of hybrid computing power with cloud + edge.

AI is rapidly shifting from the previous single-pole model of “computing power relying on cloud centers” to a multi-layer architecture of “cloud + edge + device parallelism.” And the reason behind this is not driven by trends, but by an inevitable evolution jointly driven by technical bottlenecks, economic costs, data security, and user experience.

Over the past decade, AI’s core competition points were model size, parameter count, and training capability, so almost all technology companies rushed to build massive data centers to train bigger models. But today, what truly determines industrial adoption is not “training,” but “inference,” meaning the real-time computing capability when tens of millions of users use AI simultaneously.

As the user scale grows from millions to billions, cloud inference is not only extremely expensive, but latency also continues to rise. When tens of billions of devices generate requests at the same time, the cloud cannot possibly withstand such massive concurrent pressure no matter how it scales, which forces inference to sink down to edge nodes and terminal devices.

Whether it is Apple’s A17 Pro / M3 chips, Samsung Exynos, or Qualcomm’s latest Snapdragon series, they all treat NPU (Neural Processing Unit) performance as a core selling point, and even propose that “your phone is your local large model running center.”

This trend means that in the future, all kinds of smart terminals — phones, earbuds, watches, cars, cameras, IoT devices — will have the computing power to run AI inference independently. They can execute tasks such as speech recognition, image analysis, behavior prediction, interaction understanding locally, without uploading data to the cloud for processing. This not only makes AI faster (millisecond-level response), more energy-efficient (no long-duration network transmission), more stable (can run offline), and more importantly, it is completely changing the operating model of AI from “cloud-computing centralization” to “on-device intelligent distribution.”

The data processed by AI is extremely sensitive, including voice, face, fingerprints, location behavior, daily conversations, health information, app habits, and more. If all of this data is uploaded to the cloud, even with encrypted transmission, there is still potential leakage risk. Edge computing allows computation to be completed locally, and sensitive data does not need to leave the user’s device — only necessary inference results are returned, greatly reducing the probability of privacy exposure.

Especially in high-privacy fields such as healthcare, finance, social networking, and vehicle-to-everything connectivity, on-device AI is no longer an option, but a necessary condition for compliance and security.

The cost of cloud inference is often several times the training cost. As AI usage continues to grow, cloud inference computing power becomes a “bottomless pit.” By pushing inference down to devices, enterprises can “spread” inference costs across billions of terminal hardware devices globally, greatly reducing server pressure. Industry experts even predict: in the future, 80% of AI inference will occur on terminal devices, while the cloud will mainly be responsible for training and model updates.

The value of edge computing is not merely “faster, lower latency.” It is becoming the key driving force behind the next wave of infrastructure revolution in the crypto industry. Whether it is public chain performance bottlenecks, privacy protection, AI on-chain, metaverse rendering, or latency competition in high-frequency trading, edge nodes are taking on work that previously had to be handled by cloud computing or super servers, bringing a decentralized and high-efficiency computing power structure to the blockchain ecosystem.

The following six directions are the true core areas where edge computing will profoundly reshape Web3.

Today’s public chain nodes are distributed globally, but their quality varies greatly — high latency, unstable bandwidth, RPC requests timing out easily — so many users feel that on-chain interactions are “laggy” during peak times. The emergence of edge nodes allows regional local nodes to take on the role of the access layer, shortening data traffic that originally required cross-continent access into a local transmission range of just a few milliseconds.

This means:

• RPC request responses are faster

• Wallet signing experience is smoother

• DEX depth queries and pricing are more timely

• Validator node synchronization latency is lower

• More complex on-chain interactions can be executed in real time

For scenarios like on-chain trading, GameFi operations, and real-time oracle reads, this is not just a performance improvement — it is an experience upgrade from “usable” to “silky smooth.”

Filecoin, Arweave, IO.Net, Render Network, and other DePIN projects are becoming the fastest-growing Web3 track globally, and the core of their success lies in: edge computing enables every device to become a distributed storage or computing node.

In the past, only large mining farms or professional nodes could participate. Now:

• NAS

• spare hard drives

• home broadband

• even routers and TV boxes

can all connect to decentralized networks and contribute storage and bandwidth. This real network composed of millions of global nodes will be more cost-advantageous and resilient than any centralized CDN or cloud storage, providing long-term reliable infrastructure for AI data lakes, on-chain data, and NFT assets.

For GameFi and the metaverse to truly achieve real-time interaction and multi-user dynamic rendering, relying solely on the cloud is impossible. Latency, bandwidth, and rendering load will cause the experience to collapse instantly. But with the addition of edge nodes, rendering can be completed at the node closest to the player, reducing latency by 80%.

Acceleration brought by edge rendering includes:

• real-time animation rendering for Web3 games

• regional loading of metaverse scenes

• reduced motion latency in VR/AR interactions

• complex graphics can be inferred locally

Future large-scale GameFi titles are very likely to adopt a hybrid architecture of “local rendering + on-chain settlement,” and edge computing is the core engine of this structure.

The trend in 2025 is already very clear: AI is integrating with Web3, and the key to integration is local inference + decentralized AI networks. Edge computing can achieve:

• user data is inferred locally, not uploaded to the cloud

• AI assistants are bound to DID identities

• decentralized hosting of on-chain AI models

• globally distributed collaboration of AI computing nodes

• Web3 + AI applications run under privacy protection

This means that in the future, you can have an AI assistant running on your phone. It will not hand your data to any company, but provide personalized services for you based on your on-chain identity — super private, super secure.

Crypto HFT bots, arbitrage systems, and liquidity market makers are, just like traditional finance, entering “millisecond-level competition.” The lower the latency, the higher the profit. Edge nodes can:

• route trading requests in advance to the nearest data center

• shorten latency for DEX quote refresh and oracle update

• increase arbitrage success rate and MEV front-running success rate

• reduce transaction failures caused by network congestion

If exchanges (such as SuperEx) deploy edge architecture in the future, user trading latency can be reduced to an experience several times faster than traditional cloud architecture.

DID will become an important infrastructure of the Web3 ecosystem in the future, but users’ identity data is often extremely private, including:

• biometric information

• behavior patterns

• wallet preferences

• personal on-chain footprints

If all of this data is uploaded to the chain or centralized servers, the risk is extremely high. Edge computing enables DID to achieve:

• all sensitive information is processed locally

• only zero-knowledge proofs are written on-chain

• no raw data is exposed

• can achieve fully private personalized services together with AI

This will be a key breakthrough point combining privacy computing and blockchain, and is regarded as one of the most valuable directions in the future crypto industry.

AI, Web3, and IoT will all rely on it. In the future, there are three trends most worth paying attention to:

In the future, every device may be assetized and become a node that can mine and earn rewards, which will promote:

• more democratic allocation of computing power

• more secure Web3 networks

• users become part of the network, rather than passive consumers

AI edge inference + Web3 incentive models will become the core driving force for application deployment. Your phone will:

• do AI inference

• do cryptographic signing

• participate in node networks

• earn rewards

A truly decentralized world starts from here.

All applications with strong real-time requirements will move onto edge nodes:

• trading

• cross-chain communication

• real-time oracles

• Layer2 DA modules

• wallet cryptographic computation

This will make the entire Web3 faster and lower cost.

• Cloud computing created the golden decade of Web2;

• Edge computing will create the next golden decade of AI + Web3.

It will become:

• the inference engine of AI

• the low-latency network of Web3

• the computing power foundation of DePIN

• the underlying architecture of global trading systems

• the “main artery” of the future digital economy

Edge computing is not only a technological trend, but also a key direction for future global deployment, Web3 decentralized community building, and trading infrastructure upgrades. Future users will not realize the existence of edge computing, but they will feel:

• faster trading

• safer data

• smarter AI

• freer Web3

• stronger network performance

This is the true value of edge computing.