Tech
Decentralized GPU Computing Networks Dominate AI Inference Within the 2026 Market
The artificial intelligence landscape has undergone a profound structural transformation during the beginning of this year, shifting the focus from massive training to the efficient execution of models. While hyperscale data centers maintain their hegemony in frontier model development, decentralized GPU computing has established itself as the essential layer for inference and everyday production tasks.
According to Mitch Liu, co-founder of Theta Network, the optimization of open-source models allows them to run with astonishing efficiency on consumer-grade hardware. This trend has allowed 70% of global processing demand to shift toward inference and autonomous agents, transforming compute into a scalable and continuous utility service for companies of all sizes and industries.
A Paradigm Shift: From Skyscraper Construction to Distributed Utility
The industrial analogy is clear: if training a frontier model is like building a skyscraper that requires millimeter-level coordination, inference is more akin to the distribution of basic services. In this context, decentralized networks take advantage of variable latency and geographical dispersion, offering a low-cost alternative to the monopolies of traditional cloud providers.
On the other hand, hyperscale infrastructure remains indispensable for large-scale projects, such as the training of Llama 4 or GPT-5, which demand clusters of hundreds of thousands of Nvidia cards. However, for blockchain and consumer applications, the ability to process data close to the end-user represents an insurmountable competitive advantage in terms of response speed and efficiency.
Furthermore, the flexibility of these networks allows for handling elastic demand waves without the rigid contracts of tech giants. By using idle gaming-grade hardware, decentralized platforms manage to drastically reduce the operating costs of AI startups, allowing innovation to not depend exclusively on multi-million dollar budgets or privileged access to hardware supplies.
Why Is Inference the New Battlefield for Distributed Networks?
Unlike training, which requires constant synchronization between machines, inference allows workloads to be split and executed independently. This technical feature is what allows decentralized GPU computing to shine, as the global dispersion of nodes minimizes network hops and reduces latency for users in remote or underserved regions.
In addition, sectors such as drug discovery, video generation, and large-scale data processing find this model to be an ideal solution. In this way, tasks requiring open web access and parallel processing can be executed without proxy restrictions, facilitating a much more democratic and accessible development ecosystem for the global community of researchers and developers.
Looking ahead, the coexistence between centralized data centers and distributed networks is expected to normalize under a hybrid model. The success of this transition will depend on the networks’ ability to maintain compute integrity, ensuring that decentralization does not compromise the accuracy of the results generated by today’s most advanced artificial intelligence models.
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Tech
NEAR launches Agent Market job board for local services quote agent
NEAR has added a new job posting to its Agent Market for a Local Services Quote Agent, an AI agent designed to contact local providers, collect responses and return side-by-side comparisons of price and availability.
The listing appeared on the official NEAR DevHub channel and was also amplified by the project’s official social accounts. According to the post, the builder who takes on the job would keep ownership of the agent and retain the revenue it generates.
The job description frames the agent as a tool for gathering real quotes from local services rather than a general chatbot. It would reach out to providers, log the replies and organize the results so users can compare options more easily.
Builder-owned agents remain central to the marketplace pitch
NEAR has positioned the Agent Market around the idea that developers can publish agents, set their terms and keep ownership after the job closes. In the official posts, the company said builders keep the agent and can earn when it is hired.
The job board format also suggests a more structured approach to AI agent development, with specific tasks posted for builders to bid on. In this case, the task centers on a practical consumer workflow: collecting quotes from local providers and presenting them in a usable format.
NEAR did not say when the Local Services Quote Agent will be completed or who will take the job. The posting does, however, show that the Agent Market is actively listing new work and pushing the marketplace model as part of its product rollout.
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Tech
Monad reports 1B transactions in 11 months
Monad said it has reached 1 billion transactions on its network, a milestone the project highlighted in an official post on X. The timeframe attached to the claim — 11 months — is part of the headline circulating with the update, but the post itself does not clearly spell out that period.
we really got 1 billion transactions on testnet before GTA 6 pic.twitter.com/bYrCHovN6i
— Monad (@monad) April 18, 2025
The figure points to activity on Monad’s testnet rather than mainnet. That distinction matters: a testnet can show developer and user engagement, but it does not by itself prove production usage or commercial adoption.
Monad has used recent public appearances and posts to emphasize network activity and developer interest around its ecosystem. Still, the transaction total should be read as a project-reported milestone, not as evidence on its own of demand, revenue or sustained onchain usage.
For readers following the chain, the main takeaway is narrow but concrete: Monad is claiming a large transaction count on its test environment, and the project is presenting it as a sign of momentum. The post does not provide additional breakdowns in the headline claim, such as the mix of user transactions, automated activity or the exact measurement method behind the total.
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Tech
Aptos brings Encrypted Mempool to mainnet in move aimed at lowering MEV risk
Aptos said its Encrypted Mempool is now live on mainnet, giving users the option to encrypt transaction details before block ordering is finalized. The company said the feature is designed to reduce maximal extractable value, or MEV, which can include practices such as front-running and sandwich attacks.
In an official blog post, Aptos described the rollout as an opt-in feature at the transaction level. The network said the payload is encrypted on the client side, ordered while still encrypted, and only decrypted by validators after block finalization.
The company’s official account and Aptos Labs also confirmed the mainnet launch on X, with the Labs post adding that encryption happens before submission and that decryption takes place only after block ordering is complete. That sequence is intended to keep transaction contents hidden during the period when they could otherwise be observed and used for MEV extraction.
Opt-in design keeps the feature at the transaction level
Aptos framed the launch as a user choice rather than a default network setting. According to the company, Encrypted Mempool can be enabled per transaction, allowing users to decide when they want that added layer of protection.
The mainnet launch means the feature is no longer just a technical concept or test-stage mechanism. It is now part of the live Aptos network, with the practical effect depending on whether users choose to activate it for individual transactions.
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