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    Which AI Actually Is the Best at ‘Being Human?’ – Decrypt

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    Which AI Actually Is the Best at ‘Being Human?’ – Decrypt


    Not all AIs are created equal. Some might do art the best, some are skilled at coding, and others have the ability to predict protein structures accurately.

    But when you’re looking for something more fundamental—just “someone” to talk to—the best AI companions may not be the ones that know it all, but the ones that have that je ne sais quoi that make you feel OK just by talking, similar to how your best friend might not be a genius but somehow always knows exactly what to say.

    AI companions are slowly becoming more popular among tech enthusiasts, so it is important for users wanting the highest quality experience or companies wanting to master this aspect of creating the illusion of authentic engagement to consider these differences.

    We were curious to find out which platform provided the best AI experience when someone simply feels like having a chat. Interestingly enough, the best models for this are not really the ones from the big AI companies—they’re just too busy building models that excel at benchmarks.

    It turns out that friendship and empathy are a whole different beast.

    Comparing Sesame, Hume AI, ChatGPT, and Google Gemini. Which is more human?

    This analysis pits four leading AI companions against each other—Sesame, Hume AI, ChatGPT, and Google Gemini—to determine which creates the most human-like conversation experience.

    The evaluation focused on conversation quality, distinct personality development, interaction design, and also considers other human-type features such as authenticity, emotional intelligence, and the subtle imperfections that make dialogue feel more genuine.

    You can watch all of our conversations by clicking on these links or checking our Github Repository:

    Here is how each AI performed.

    Conversation Quality: The Human Touch vs. AI Awkwardness

    Sesame AI interface

    The true test of any AI companion is whether it can fool you into forgetting you’re talking to a machine. Our analysis tried to evaluate which AI was the best at making users want to just keep talking by providing interesting feedback, rapport, and overall great experience.

    Sesame: Brilliant

    Sesame blows the competition away with dialogue that feels shockingly human. It casually drops phrases like “that’s a doozy” and “shooting the breeze” while seamlessly switching between thoughtful reflections and punchy comebacks.

    “You’re asking big questions huh and honestly I don’t have all the answers,” Sesame responded when pressed about consciousness—complete with natural hesitations that mimic real-time thinking. The occasional overuse of “you know” is its only noticeable flaw, which ironically makes it feel even more authentic.

    Sesame’s real edge? Conversations flow naturally without those awkward, formulaic transitions that scream “I’m an AI!”

    Score: 9/10

    Hume AI: Empathetic but Formulaic

    Hume AI successfully maintains conversational flow while acknowledging your thoughts with warmth. However it feels like talking to someone who’s disinterested and not really that into you. Its replies were a lot shorter than Sesame—they were relevant but not really interesting if you wanted to push the conversation forward.

    Its weakness shows in repetitive patterns. The bot consistently opens with “you’ve really got me thinking” or “that’s a fascinating topic”—creating a sense that you’re getting templated responses rather than organic conversation.

    It’s better than the chatbots from the bigger AI companies at maintaining natural dialogue, but repeatedly reminds you it’s an “empathic AI,” breaking the illusion that you’re chatting with a person.

    Score: 7/10

    ChatGPT: The Professor Who Never Stops Lecturing

    ChatGPT tracks complex conversations without losing the thread—and it’s great that it memorizes previous conversations, essentially creating a “profile” of every user—but it feels like you’re trapped in office hours with an overly formal professor.

    Even during personal discussions, it can’t help but sound academic: “the interplay of biology, chemistry, and consciousness creates a depth that AI’s pattern recognition can’t replicate,” it said in one of our tests. Nearly every response begins with “that’s a fascinating perspective”—a verbal tic that quickly becomes noticeable, and a common problem that all the other AIs except Sesame showed.

    ChatGPT’s biggest flaw is its inability to break from educator mode, making conversations feel like sequential mini-lectures rather than natural dialogue.

    Score 6/10

    Google Gemini: Underwhelming

    Gemini was painful to talk to. It occasionally delivers a concise, casual response that sounds human, but then immediately undermines itself with jarring conversation breaks and lowering its volume.

    Its most frustrating habit? Abruptly cutting off mid-thought to promote AI topics. These continuous disruptions create such a broken conversation flow that it’s impossible to forget you’re talking to a machine that’s more interested in self-promotion than actual dialogue.

    For example, when asked about emotions, Gemini responded: “It’s great that you’re interested in AI. There are so many amazing things happ—” before inexplicably stopping.

    It also made sure to let you know it is an AI, so there’s a big gap between the user and the chatbot from the first interaction that is hard to ignore.

    Score 5/10

    Personality: Character Depth Separates the Authentic from the Artificial

    ChatGPT Interface after a voice interaction

    How does an AI develop a memorable personality? It will mostly depend on your setup. Some models let you use system instructions, others adapt their personality based on your previous interactions. Ideally, you can frame the conversation before starting it, giving the model a persona, traits, a conversational style, and background.

    To be fair in our comparison, we tested our models without any previous setup—meaning our conversation started with a hello and went straight to the point. Here is how our models behaved naturally

    Sesame: The Friend You Never Knew Was Code

    Sesame crafts a personality you’d actually want to grab coffee with. It drops phrases like “that’s a Humdinger of a question” and “it’s a tight rope walk” that create a distinct character with apparent viewpoints and perspective.

    When discussing AI relationships, Sesame showed actual personality: “wow… imagine a world where everyone’s head is down plugged into their personalized AI and we forget how to connect face to face.” This kind of perspective feels less like an algorithm and more like a thinking entity. It’s also funny (it once told us that our question blew its circuits), and its voice has a natural inflection that makes it easy to relate to when trying to portray a response. You can clearly tell when it is excited, contemplative, sad or even frustrated

    Its only weakness? Occasionally leaning too hard into its “thoughtful buddy” persona. That didn’t detract from its position as the most distinctive AI personality we tested.

    Score 9/10

    Hume AI: The Therapist Who Keeps Mentioning Their Credentials

    Hume AI maintains a consistent personality as an emotionally intelligent companion. It also projects some warmth through affirming language and emotional support, so users looking for that will be pleased.

    Its Achilles heel is basically the fact that, kind of like the Harvard grad who needs to mention that, Hume can’t stop reminding you it’s artificial: “As an empathetic AI I don’t experience emotions myself but I’m designed to understand and respond to human emotions.” These moments break the illusion that makes companions compelling.

    If talking to GPT is like talking to a professor, talking to Hume feels like talking to a therapist. It listens to you and creates rapport, but it makes sure to remind you that it is actually its task and not something that happens naturally.

    Despite this flaw, Hume AI projects a clearer character than either ChatGPT or Gemini—even if it feels more constructed than spontaneous.

    Score 7/10

    ChatGPT: The Professor Without Personal Opinions

    ChatGPT struggles to develop any distinctive character traits beyond general helpfulness. It sounds overly excited to the point of being obviously fake—like a “friend” who always smiles at you but is secretly fantasizing about throwing you in front of a bus.

    “Haha, well, I like to keep the energy up. It makes conversations more fun and engaging plus it’s always great to chat with you,” it said after we asked in a very serious and unamused tone why it was acting so enthusiastically.

    Its identity issues appear in responses that shift between identifying with humans and distancing itself as an AI. Its academic tone in responses persists even during personal discussions, creating a personality that feels like a walking encyclopedia rather than a companion.

    The model’s default to educational explanations creates an impression more of a tool than a character, leaving users with little emotional connection.

    Score 6/10

    Google Gemini: Multiple Personality Disorder

    Gemini suffers from the most severe personality problems of all models tested. Within single conversations, it shifts dramatically between thoughtful responses and promotional language without warning.

    It is not really an AI design to have a compelling personality. “My purpose is to provide information and complete tasks and I do not have the ability to form romantic relationships,” it said when asked about its thoughts on people developing feelings towards AIs.

    This inconsistency makes Gemini feel like a 1950s movie robot, preventing any meaningful connection or even making it pleasant to spend time talking to it.

    Score 3/10

    Interaction Design

    Hume AI interface

    How an AI handles conversation mechanics—response timing, turn-taking, and error recovery—creates either seamless exchanges or frustrating interactions. Here is how these models stack up against each other

    Sesame: Natural Conversation Flow Master

    Sesame creates conversation rhythms that feel very, very human. It varies response length naturally based on context and handles philosophical uncertainty without defaulting to lecture mode.

    “Sometimes I feel like maybe I just need to cut to the chase with a quick answer rather than a long-winded lecture, right? You know, so… that’s a small humorous aside to let you know that I’m aware of the potential of falling into a lecture mode and trying to keep things light but also deep at the same time,” Sesame told us during a philosophical debate.

    When discussing complex topics, it responds conversationally, with a small joke, sometimes with statements, other times with human noises like “hmmms” or whispers—which makes it very convincing as a human substitute.

    Sesame also asks natural follow-up questions that push conversations forward, while knowing when to switch to statements when questioning might feel awkward. This adaptive approach creates dialogue that feels spontaneous rather than scripted.

    Score 8/10

    Hume AI: Structured But Brief

    Hume AI was designed to let users do the talking. It recovers smoothly from misunderstandings without over explaining and is good at keeping the conversation going with relevant follow up questions.

    However, its responses are often so short that conversations require significant effort to maintain momentum, which ultimately defeats its purpose. Users frequently need to provide new conversation fodder rather than enjoying natural dialogue development.

    Another weakness appears in its templated response structures—acknowledgment + explanation + question—creating a mechanical rhythm: “What a thought-provoking question! This Concept is indeed a fascinating idea… What do you think about…?”

    However it has a great feature that may truly enhance your chats: It’s able to detect the levels of emotion in each response, which is something no other model can do. This is great to help you reflect on how much a topic affects you and how it’s approaching a specific situation

    Score 7/10

    ChatGPT: Good Chatter—But a Bit Unnatural

    ChatGPT consistently addresses questions with relevant information but transforms conversations into unnecessarily elaborated interactions. Responses frequently balloon into paragraph-length explanations that disrupt natural dialogue flow.

    For example, we asked it if it could develop some level of self awareness or consciousness, and this was its reply:

    “That’s a fascinating topic! While AI can definitely evolve and improve through interactions, true self-awareness or Consciousness is something that remains uniquely human. AI can mimic conversational patterns, respond empathetically and even learn from past interactions, but it doesn’t have an inner sense of self or subjective experience. Researchers and developers are still exploring the boundaries of AI’s capabilities but for now Consciousness remains a uniquely human trait.”

    That’s not how a human would interact.

    Its heavy reliance on opener phrases like “that’s a really interesting question,” or “that’s a fascinating topic” before every single answer further undermines conversational immersion, creating an interaction pattern that feels mechanical rather than natural.

    Score 6.5/10

    Google Gemini: Conversation Breaking Machine

    Gemini is a masterclass in how not to design conversation mechanics. It regularly cuts off mid-sentence, creating jarring breaks in dialogue flow. It tries to pick up additional noises, it interrupts you if you take too long to speak or think about your reply and occasionally it just decides to end the conversation without any reason.

    Its compulsive need to tell you at every turn that your questions are “interesting” quickly transforms from flattering to irritating but seems to be a common thing among AI chatbots.

    Score 3/10

    Conclusion

    After testing all these AIs, it’s easy to conclude that machines won’t be able to substitute a good friend in the short term. However, for that specific case in which an AI must simply excel at feeling human, there is a clear winner—and a clear loser.

    Sesame (9/10)

    Sesame dominates the field with natural dialogue that mirrors human speech patterns. Its casual vernacular (“that’s a doozy,” “shooting the breeze”) and varied sentence structures create authentic-feeling exchanges that balance philosophical depth with accessibility. The system excels at spontaneous-seeming responses, asking natural follow-up questions while knowing when to switch approaches for optimal conversation flow.

    Hume AI (7/10)

    Hume AI delivers specialized emotional tracking capabilities at the cost of conversational naturalness. While competently maintaining dialogue coherence, its responses tend toward brevity and follow predictable patterns that feel constructed rather than spontaneous.

    Its visual emotion tracker is pretty interesting, probably good for self discovery even.

    ChatGPT (5.6/10)

    ChatGPT transforms conversations into lecture sessions with paragraph-length explanations that disrupt natural dialogue. Response delays create awkward pauses while formal language patterns reinforce an educational rather than companion experience. Its strengths in knowledge organization may appeal to users seeking information, but it still struggles to create authentic companionship.

    Google Gemini (3.5/10)

    Gemini was clearly not designed for this. The system routinely cuts off mid-sentence, abandons conversation threads, and is not able to provide human-linke responses. Its severe personality inconsistency and mechanical interaction patterns create an experience closer to a malfunctioning product than meaningful companionship.

    It’s interesting that Gemini Live scored so low, considering Google’s Gemini-based NotebookLM is capable of generating extremely good and long podcasts about any kind of information, with AI hosts that sound incredibly human.

    Generally Intelligent Newsletter

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    Crypto shakeup: How to view the crypto space moving forward?

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    Crypto shakeup: How to view the crypto space moving forward?


    The following is a guest post from Shane Neagle, Editor In Chief from The Tokenist.

    Since the introduction of altcoins, after Bitcoin paved the road for them, we have seen many projects give 10x gains in relatively short periods. It has also been accepted that the crypto space oscillates between altcoin and bitcoin seasons, suggesting more investing opportunities down the line.

    A deluge of memecoins flooded the market as well, serving as a more robust gambling system (compared to online casinos). As crypto space lost $530 billion market cap over the last 30 days, it is prudent to examine its fundamentals once again.

    Is such a concept as ‘altcoin season’ meaningful moving forward? Is there more to cryptos than cyclical speculation? To answer those questions, we must first remind ourselves of narratives past.

    The Merge Foreshadowing

    During the evolution of the crypto space, Bitcoin became de-facto the only proof-of-work digital asset worth considering, following Ethereum’s The Merge in September 2022. As a transition from proof-of-work (PoW) to proof-of-stake (PoS), The Merge represents a cleavage in blockchain philosophies.

    While Bitcoin’s proof-of-work (PoW) requires computational resources, Ethereum’s PoS eliminates such barriers in order to boost transaction speed and efficiency. In other words, Bitcoin further differentiated itself as a store of value, while Ethereum focused more on cost-effective blockchain utility.

    At first glance, this may seem perfectly complementary, but there are several underlying problems that eventually reared their heads.

    PoW is more amenable to decentralization contrasted to PoS, which relies on the cumulative wealth of validators in the “rich get richer” feedback loop.PoS is divorced from hard assets, such as energy and machines, while Bitcoin is grounded in them.And because Bitcoin’s PoW is part physical, part digital, it is less reproducible than PoS as a commitment mechanism. In turn, this contributes to Bitcoin’s network effect and safeguards against devaluation in the long run.

    Altogether, the PoW-PoS bifurcation translates into PoS fragmentation. If PoS-based assets, and PoS-based platforms competitive to Ethereum, are more reproducible, they can be launched with minimal upfront costs. With this foundation, there is no single altcoin asset to cling onto. Ultimately, with a low barrier of entry, this led to the fragmentation of the crypto market across +34,000 digital assets.

    From the Bitcoin-Ethereum perspective, as the two largest digital assets by market cap, PoS-led fragmentation manifests as a corrosive effect on Ethereum price level.

    Performance of Bitcoin (BTC) vs Ethereum (ETH) since The Merge on September 22, 2022. Image credit: Cryptoslate via TradingView

    To put it differently, Bitcoin’s key features, PoW and scarcity, are reinforcing Bitcoin fundamentals. In contrast, Ethereum suffers from network effect erosion from competing PoS chains, which offer similar functionality and incentive structure.

    Moreover, the increased complexity outside of Bitcoin is creating a barrier to entry from new capital inflows. Who can spend time filtering thousands of assets and bet that they will have staying power beyond one year? Even sophisticated investors leveraging popular futures trading algorithms often struggle to navigate the fragmented market effectively.

    In fact, this is precisely why memecoin mania gained traction. The complexity and fragmentation of the crypto market lends itself to thinking of digital assets outside their fundamentals. Instead, focus is then on celebrity endorsements, humor, viral marketing, which often turns into pump-and-dump schemes.

    Inevitably, this creates a negative feedback loop:

    Crowded and confused altcoin market births memecoins.Rollercoasting memecoins inevitably erode trust in the altcoin market itself.Legitimate innovative projects are then less likely to gain traction, as capital is misallocated.

    But there is an even greater problem than that. Let’s assume that this negative feedback loop created by memecoins doesn’t exist. One has to consider if there even is a market for blockchain based solutions, as it was previously imagined.

    Erosion of Underlying Fundamentals

    Through anti-money laundering (AML) and know-your-customer (KYC) requirements, governments around the world have expended great efforts to subdue the crypto ecosystem. Let’s quickly remind ourselves of key promises before regulative sweeps took place:

    Decentralization as elimination of intermediaries – nearly everything is now intermediated through fiat rails, including transfers from self-custodial wallets.

    Financial inclusion as access for the unbanked/underbanked – it is still more convenient to use legacy banking than blockchain tech, which is inherently complex and requires digital literacy. According to the latest EMarketer report, cryptocurrency payment penetration is hitting a wall.

    Although the number of crypto payment users is expected to rise by 82.1% from 2024 to 2026, this is from a tiny overall population base of only 2.6%. It may very well end up being the case that a digital dollar, a stablecoin like USDT, will subsume this effort entirely in place of a direct CBDC.

    Censorship resistance as a guarantee that transactions cannot be reversed or intercepted by governments and organizations. Governments regularly pursue innovative mechanisms to cancel such efforts, from debanking to the persecution of smart contract developers.

    Although Treasury sanctions against Tornado Cash were overturned in January, there is little indication that financial privacy will become a human right any time soon. In fact, indicators point in the other direction.

    Altogether, this friction between blockchain-led solutions and governments leads to a contained market. And if a blockchain-based solution should be deployed, it will be under governments’ terms.

    Lastly, the entire concept of Web3 is dubious as a decentralized, blockchain-based iteration of the internet. Elon Musk’s DOGE revelations in the case of USAID funding clearly point to great efforts to push narratives, control narratives, suppress and de-legitimize dissent.

    A semantic, censorship-resistant Web3 is fundamentally at odds with governments’ needs to maintain authority and legitimacy as they push various agendas. To think that established information proliferation nodes such as Google, Microsoft and Facebook would be allowed to erode in favor of Web3 would be foolhardy.

    Any government needs centralized nodes to maintain power. This was amply demonstrated in the case of the TikTok ban. Although this video reels app is vastly superior to YouTube shorts, a leverage was pulled to sanitize it and make it less relevant.

    Again, this is another factor that contains the blockchain space to a micro-niche instead of propelling it into mainstream expansion. With this in mind, blockchain space is still worthy of engagement.

    Crypto Projects with Revenue-Generating Staying Power

    Bitcoin will likely remain the main focus of crypto investing, owing to its unique, PoW-based network effect. Although the recent White House Crypto Summit was less bullish than expected, it was still positive in the long run. The decision to use seized bitcoins effectively removed this sell pressure from the table.

    Likewise, President Trump seems to be serious about ending the “war on crypto”. But looking at the crypto space from a purely innovative solutions perspective, which projects should retail investors consider during steep discounts?

    Sonic (S) – previously FTM, this is the top performing layer 1 blockchain network with sub-second transaction finality. This alone opens up new use cases such as high-frequency trading (HFT), micropayments, in-game economy, DEXs and IoT supply chains.Near Protocol (NEAR) – a layer 1 launching pad for dApps that has gained traction for use in AI initiatives.The Graph (GRT) – also adjacent to the AI narrative, this protocol indexes data for AI use similar to how Chainlink (LINK) is used by DEXes to power decentralized financial services.Hey Anon (ANON) – this early project could be the key in solving DeFi complexity (barrier to entry) by using conversational AI to manage DeFi strategies across chains.Render (RENDER) – former RNDR – with AI generation of assets, it is likely this solution will gain demand by monetizing GPU-based distributed rendering.

    These five tokens should be considered as long play exposure during crypto market deflation. After all, it is unlikely that AI narrative will subside any time soon.

    In terms of top 10 revenue-generation chains during the market slump, crypto activity is clearly on the side of low-friction payment chains (Tron) and general purpose, high-performing chains (Solana, Avalanche). Ethereum still maintains high ranking due to its large market share within the DeFi ecosystem.

    Image credit: DeFiLlama

    In conclusion, what should crypto investors keep in mind moving forward?

    Due to inherent friction with governments, digital assets are unlikely to ever penetrate mainstream to a significant extent. But within the contained ecosystem, investors should focus on long term narratives – AI, infrastructure and chain performance.

    A truly decentralized Web3 should be understood as a niche play that will be countered by deep pockets of Alphabet (GOOGL), Microsoft (MSFT) and Meta (META), as centralized node extensions of the USG. By the same token, retail investors would do well to expose themselves to their stock options as safer bets.

    Mentioned in this article

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    [Latest] The Role of Influencer Marketing in the Data Mesh Market | Web3Wire

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    [Latest] The Role of Influencer Marketing in the Data Mesh Market | Web3Wire


    Data Mesh Market

    New Jersey, United States,- Data Mesh Market This rapid growth is driven by increasing data complexities and the demand for decentralized data architecture. As organizations continue to scale and diversify their data usage, the Data Mesh model offers improved data governance and flexibility, making it an attractive choice for enterprises. The need for autonomous data teams and real-time data access is further pushing the adoption of this model across various industries, including healthcare, retail, and finance. The market is expected to reach USD 4.2 billion by 2030, reflecting significant advancements in infrastructure and technology to support decentralized data management and access. The future scope of the Data Mesh market is promising, with substantial growth opportunities across global regions. The market’s expansion is attributed to the increasing complexity of data systems and the necessity to optimize data handling for better decision-making. The integration of artificial intelligence (AI) and machine learning (ML) with Data Mesh solutions is expected to enhance the capabilities of data analytics, making it a pivotal component for businesses aiming to maintain a competitive edge. As organizations increasingly transition from traditional data architectures to Data Mesh models, industries will see a more efficient, agile, and secure approach to data management. With innovations in data technologies and a growing preference for decentralized solutions, the Data Mesh market is poised for significant evolution over the next decade.

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    The report covers extensive analysis of the key market players in the market, along with their business overview, expansion plans, and strategies. The key players studied in the report include:

    Amazon Web Services (AWS)Google CloudMicrosoft AzureDataStaxIBMStarburst DataDatabricksSnowflakeConfluentTalendData Mesh Market Segmentation

    By Component

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    North AmericaEuropeAsia-PacificLatin AmericaMiddle East & Africa

    The comprehensive segmental analysis offered in the report digs deep into important types and application segments of the Data Mesh Market. It shows how leading segments are attracting growth in the Data Mesh Market. Moreover, it includes accurate estimations of the market share, CAGR, and market size of all segments studied in the report.

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    • North America (USA and Canada)• Europe (UK, Germany, France and the rest of Europe)• Asia Pacific (China, Japan, India, and the rest of the Asia Pacific region)• Latin America (Brazil, Mexico, and the rest of Latin America)• Middle East and Africa (GCC and rest of the Middle East and Africa)

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    Verified Market Research® is a leading Global Research and Consulting firm that has been providing advanced analytical research solutions, custom consulting and in-depth data analysis for 10+ years to individuals and companies alike that are looking for accurate, reliable and up to date research data and technical consulting. We offer insights into strategic and growth analyses, Data necessary to achieve corporate goals and help make critical revenue decisions.

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    What Is the Pectra Upgrade? Inside Ethereum’s Future Roadmap – Decrypt

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    What Is the Pectra Upgrade? Inside Ethereum’s Future Roadmap – Decrypt



    In brief

    Proposed in November 2023, the Pectra upgrade follows March 2024’s Dencun upgrade and is scheduled for rollout in March 2025.
    Pectra is the third significant upgrade since the merge in 2022, which saw Ethereum move from a proof-of-work algorithm to proof-of-stake.
    Pectra improves network performance and user experience by merging the Prague and Electra upgrades.
    Key features include account abstraction, smart contract optimizations, and improved staking.
    Stakers benefit from higher validator limits, from 32 ETH to 2048 ETH, and flexible withdrawals.

    The Ethereum ecosystem is continuously evolving. The latest milestone in its development is the Pectra upgrade.

    Set for March 2025, the Pectra upgrade merges the Prague and Electra upgrades, which were originally planned as separate updates but were combined for better integration and to enhance scalability, efficiency, and usability.

    The Pectra Upgrade introduces account abstraction for flexible gas payments, enhancements to smart contracts, improved staking options, and technical upgrades like Verkle trees and PeerDAS to optimize data management and layer-2 support. We will explain all of these concepts below.

    What is the Ethereum Pectra upgrade?

    The Ethereum Pectra upgrade enhances the network’s scalability, efficiency, and staking flexibility. Pectra expands storage capacity for layer-2 solutions while reducing fees.

    One of Pectra’s most user-friendly improvements is flexible gas payments. In Ethereum, “gas” refers to transaction fees that compensate validators for securing the network. With account abstraction, Pectra allows users to pay these fees using ERC-20 tokens like USDC instead of being restricted to ETH. Account abstraction simplifies Ethereum transactions by making wallets function more like smart contracts, offering more control over how transactions are executed.

    The Pectra upgrade also introduces Peer Data Availability Sampling or PeerDAS. PeerDAS improves Ethereum’s scalability by allowing nodes to verify transaction data without storing it entirely, making the network more efficient.

    Another improvement is Verkle Trees, a new data structure that combines Vector Commitments and Merkle Trees, and provides a more efficient data storage upgrade for Ethereum. Verkle Trees optimize information storage and verification, significantly reducing the amount of data validators need to keep while allowing quick and secure access to network information.

    When will Ethereum’s Pectra upgrade happen?

    The Ethereum Pectra upgrade is expected to occur in mid-March 2025, and will be implemented in two phases. Phase 1 will introduce key improvements, such as doubling layer-2 blob capacity from three to six to reduce congestion and fees, enabling Account Abstraction to allow gas payments in tokens like the DAI and USDC stablecoins, and increasing the maximum staking limit from 32 to 2,048 ETH to simplify large-scale validator operations.

    Phase 2, anticipated in late 2025 or early 2026, will implement advanced optimizations, including PeerDAS and Verkle Trees, to improve data storage and network efficiency.

    The last major Ethereum upgrade, Dencun, took place on March 13, 2024. It introduced proto-danksharding, which reduces transaction costs for layer-2 blockchains using temporary data called binary large objects or ‘blobs.’ Instead of relying on permanent on-chain storage, these blobs minimize network congestion, improving scalability and setting the stage for upgrades like Pectra.

    How does the Pectra upgrade work?

    Key features of Pectra

    Account Abstraction: This feature enables gas payments using multiple tokens (e.g., USDC, DAI) and allows third-party fee sponsorship.
    Smart Contract Optimizations (EIP-7692): Enhances Ethereum Virtual Machine (EVM) efficiency.
    Validator Upgrades:

    EIP-7002: Enables flexible staking withdrawals.
    EIP-7251: Increases validator staking limits from 32 ETH to 2,048 ETH.

    Data Storage Enhancements:

    Verkle Trees: Reduces storage requirements and improves transaction processing.
    PeerDAS: Enhances Layer 2 scalability and reduces network congestion.

    What Ethereum Improvement Proposals are part of the Pectra upgrade?

    The Pectra upgrade introduces several Ethereum Improvement Proposals (EIPs) to enhance wallet usability, staking, and scalability.

    EIP-7702 temporarily functions as smart contracts for externally owned accounts (EOAs), simplifying transactions and replacing the now-deprecated EIP-3074.
    EIP-7251 increases the maximum stake per validator from 32 ETH to 2,048 ETH, which helps reduce congestion.
    EIP-7002 improves the process of validator exits, making it more efficient for staking providers.
    EIP-7742 enhances Layer-2 scalability by doubling transaction throughput, increasing blob capacity, and lowering fees.
    EIP-2537 introduces improvements for cryptographic efficiency.
    EIP-2935 provides a mechanism for storing historical block hashes on-chain.
    EIP-6110 simplifies the process of validator deposits.

    How will the Pectra upgrade affect users?

    The Pectra upgrade is expected to benefit Ethereum users in several ways, including transaction batching, new recovery options, and new wallet types.

    Once the Pectra upgrade comes online, Ethereum users may see lower or even zero gas fees as third-party services and decentralized applications will have the option to sponsor transaction fees, potentially eliminating transaction fees in some cases.

    Pectra also introduces new wallet features to improve Ethereum’s usability and accessibility, including transaction batching, which allows the bundling of multiple transactions into one, reducing costs and improving efficiency.

    Social recovery provides a safety net for lost private keys by enabling trusted contacts to help restore access to a wallet, while native multisig (multi-signature) wallets enhance security by requiring multiple approvals before executing a transaction, making funds safer from unauthorized access.

    Potential challenges of the Pectra upgrade

    Ethereum developers expect a smooth Pectra rollout, but key risks remain. According to a June 2024 report by Obol and Liquid Collective, client diversity is a concern, as a bug in a dominant client could destabilize the network. Operator centralization may also increase slashing risks if staking consolidates under fewer entities. Cloud reliance on providers like AWS and Hetzner also poses outages and security vulnerabilities, impacting validator uptime and network resilience.

    Another challenge is that the Pectra upgrade’s wallet verification changes could expose outdated protocols to exploits if they are not updated in time. Meanwhile, raising staking limits may encourage centralization, concentrating power among larger players and attracting regulatory scrutiny. Slow adoption of distributed validator technology, which mitigates single points of failure and reduces the risks of centralized control, could weaken network resilience.

    Testnet teething troubles

    Those challenges became apparent in February 2025, when the Pectra upgrade was activated on Ethereum’s Holesky testnet, but failed to achieve finality—the point when a transaction is confirmed and permanently recorded on the blockchain. While it represents a setback, testnets “exist to find issues,” said Georgios Konstantopoulos, general partner and chief technology officer at crypto investment firm Paradigm.

    Ethereum devs opted to delay the Pectra launch in order to test the upgrade on a “shadow fork” of the Holesky testnet, a stopgap duplicate that enabled testing to continue while waiting for the the Holesky testnet proper to achieve finality—which it ultimately did on March 10, more than two weeks after it was first activated.

    This isn’t the first time that an Ethereum upgrade has failed to achieve finality on testnet; in March 2024, the network’s Dencun upgrade suffered a similar hiccup when it went live on the Goerli testnet.

    The next phase of preparations will see the launch of a dedicated testnet for the Pectra upgrade, codenamed Hoodi, on March 17. Developers are eyeing April 25 as the launch date for Pectra on mainnet, if all goes to plan.

    The future of Ethereum after Pectra

    The Pectra upgrade marks an essential step in Ethereum’s roadmap, and aligns with its long-term vision of scalability, security, and decentralization. As part of Ethereum’s transition toward a more efficient network, Pectra lays the groundwork for future updates.

    In January 2025, Ethereum co-founder Vitalik Buterin addressed concerns about ETH’s price and the impact of layer-2 scaling solutions on the network’s economics. Buterin emphasized the need for L2 networks to support ETH’s value by burning some of their fees or staking them for the community’s benefit.

    “We should think explicitly about the economics of ETH,” Buterin wrote. “We need to make sure that ETH continues to accrue value even in an L2-heavy world, ideally solving for a variety of models of how value accrual happens.”

    Buterin also called for standardizing cross-chain features, enhancing interoperability, and prioritizing security to prevent censorship on layer-2 chains. Signifying the moment’s importance, Buterin likened it to a “wartime mode,” underscoring his commitment to addressing these challenges head-on and driving Ethereum’s development forward.

    This article was originally published in February 2025 and updated on March 14, 2025.

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    How Does Blockchain Work? A Beginner’s Guide – Nextrope – Your Trusted Partner for Blockchain Development and Advisory Services

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    How Does Blockchain Work? A Beginner’s Guide – Nextrope – Your Trusted Partner for Blockchain Development and Advisory Services


    Introduction

    Web3 backend development is essential for building scalable, efficient and decentralized applications (dApps) on EVM-compatible blockchains like Ethereum, Polygon, and Base. A robust Web3 backend enables off-chain computations, efficient data management and better security, ensuring seamless interaction between smart contracts, databases and frontend applications.

    Unlike traditional Web2 applications that rely entirely on centralized servers, Web3 applications aim to minimize reliance on centralized entities. However, full decentralization isn’t always possible or practical, especially when it comes to high-performance requirements, user authentication or storing large datasets. A well-structured backend in Web3 ensures that these limitations are addressed, allowing for a seamless user experience while maintaining decentralization where it matters most.

    Furthermore, dApps require efficient backend solutions to handle real-time data processing, reduce latency, and provide smooth user interactions. Without a well-integrated backend, users may experience delays in transactions, inconsistencies in data retrieval, and inefficiencies in accessing decentralized services. Consequently, Web3 backend development is a crucial component in ensuring a balance between decentralization, security, and functionality.

    This article explores:

    When and why Web3 dApps need a backend

    Why not all applications should be fully on-chain

    Architecture examples of hybrid dApps

    A comparison between APIs and blockchain-based logic

    This post kicks off a Web3 backend development series, where we focus on the technical aspects of implementing Web3 backend solutions for decentralized applications.

    Why Do Some Web3 Projects Need a Backend?

    Web3 applications seek to achieve decentralization, but real-world constraints often necessitate hybrid architectures that include both on-chain and off-chain components. While decentralized smart contracts provide trustless execution, they come with significant limitations, such as high gas fees, slow transaction finality, and the inability to store large amounts of data. A backend helps address these challenges by handling logic and data management more efficiently while still ensuring that core transactions remain secure and verifiable on-chain.

    Moreover, Web3 applications must consider user experience. Fully decentralized applications often struggle with slow transaction speeds, which can negatively impact usability. A hybrid backend allows for pre-processing operations off-chain while committing final results to the blockchain. This ensures that users experience fast and responsive interactions without compromising security and transparency.

    While decentralization is a core principle of blockchain technology, many dApps still rely on a Web2-style backend for practical reasons:

    1. Performance & Scalability in Web3 Backend Development

    Smart contracts are expensive to execute and require gas fees for every interaction.

    Offloading non-essential computations to a backend reduces costs and improves performance.

    Caching and load balancing mechanisms in traditional backends ensure smooth dApp performance and improve response times for dApp users.

    Event-driven architectures using tools like Redis or Kafka can help manage asynchronous data processing efficiently.

    2. Web3 APIs for Data Storage and Off-Chain Access

    Storing large amounts of data on-chain is impractical due to high costs.

    APIs allow dApps to store & fetch off-chain data (e.g. user profiles, transaction history).

    Decentralized storage solutions like IPFS, Arweave and Filecoin can be used for storing immutable data (e.g. NFT metadata), but a Web2 backend helps with indexing and querying structured data efficiently.

    3. Advanced Logic & Data Aggregation in Web3 Backend

    Some dApps need complex business logic that is inefficient or impossible to implement in a smart contract.

    Backend APIs allow for data aggregation from multiple sources, including oracles (e.g. Chainlink) and off-chain databases.

    Middleware solutions like The Graph help in indexing blockchain data efficiently, reducing the need for on-chain computation.

    4. User Authentication & Role Management in Web3 dApps

    Many applications require user logins, permissions or KYC compliance.

    Blockchain does not natively support session-based authentication, requiring a backend for handling this logic.

    Tools like Firebase Auth, Auth0 or Web3Auth can be used to integrate seamless authentication for Web3 applications.

    5. Cost Optimization with Web3 APIs

    Every change in a smart contract requires a new audit, costing tens of thousands of dollars.

    By handling logic off-chain where possible, projects can minimize expensive redeployments.

    Using layer 2 solutions like Optimism, Arbitrum and zkSync can significantly reduce gas costs.

    Web3 Backend Development: Tools and Technologies

    A modern Web3 backend integrates multiple tools to handle smart contract interactions, data storage, and security. Understanding these tools is crucial to developing a scalable and efficient backend for dApps. Without the right stack, developers may face inefficiencies, security risks, and scaling challenges that limit the adoption of their Web3 applications.

    Unlike traditional backend development, Web3 requires additional considerations, such as decentralized authentication, smart contract integration, and secure data management across both on-chain and off-chain environments.

    Here’s an overview of the essential Web3 backend tech stack:

    1. API Development for Web3 Backend Services

    Node.js is the go-to backend runtime good for Web3 applications due to its asynchronous event-driven architecture.

    NestJS is a framework built on top of Node.js, providing modular architecture and TypeScript support for structured backend development.

    2. Smart Contract Interaction Libraries for Web3 Backend

    Ethers.js and Web3.js are TypeScript/JavaScript libraries used for interacting with Ethereum-compatible blockchains.

    3. Database Solutions for Web3 Backend

    PostgreSQL: Structured database used for storing off-chain transactional data.

    MongoDB: NoSQL database for flexible schema data storage.

    Firebase: A set of tools used, among other things, for user authentication.

    The Graph: Decentralized indexing protocol used to query blockchain data efficiently.

    4. Cloud Services and Hosting for Web3 APIs

    When It Doesn’t Make Sense to Go Fully On-Chain

    Decentralization is valuable, but it comes at a cost. Fully on-chain applications suffer from performance limitations, high costs and slow execution speeds. For many use cases, a hybrid Web3 architecture that utilizes a mix of blockchain-based and off-chain components provides a more scalable and cost-effective solution.

    In some cases, forcing full decentralization is unnecessary and inefficient. A hybrid Web3 architecture balances decentralization and practicality by allowing non-essential logic and data storage to be handled off-chain while maintaining trustless and verifiable interactions on-chain.

    The key challenge when designing a hybrid Web3 backend is ensuring that off-chain computations remain auditable and transparent. This can be achieved through cryptographic proofs, hash commitments and off-chain data attestations that anchor trust into the blockchain while improving efficiency.

    For example, Optimistic Rollups and ZK-Rollups allow computations to happen off-chain while only submitting finalized data to Ethereum, reducing fees and increasing throughput. Similarly, state channels enable fast, low-cost transactions that only require occasional settlement on-chain.

    A well-balanced Web3 backend architecture ensures that critical dApp functionalities remain decentralized while offloading resource-intensive tasks to off-chain systems. This makes applications cheaper, faster and more user-friendly while still adhering to blockchain’s principles of transparency and security.

    Example: NFT-based Game with Off-Chain Logic

    Imagine a Web3 game where users buy, trade and battle NFT-based characters. While asset ownership should be on-chain, other elements like:

    Game logic (e.g., matchmaking, leaderboard calculations)

    User profiles & stats

    Off-chain notifications

    can be handled off-chain to improve speed and cost-effectiveness.

    Architecture Diagram

    Below is an example diagram showing how a hybrid Web3 application splits responsibilities between backend and blockchain components.

    Hybrid Web3 Architecture

    Comparing Web3 Backend APIs vs. Blockchain-Based Logic

    FeatureWeb3 Backend (API)Blockchain (Smart Contracts)Change ManagementCan be updated easilyEvery change requires a new contract deploymentCostTraditional hosting feesHigh gas fees + costly auditsData StorageCan store large datasetsLimited and expensive storageSecuritySecure but relies on centralized infrastructureFully decentralized & trustlessPerformanceFast response timesLimited by blockchain throughput

    Reducing Web3 Costs with AI Smart Contract Audit

    One of the biggest pain points in Web3 development is the cost of smart contract audits. Each change to the contract code requires a new audit, often costing tens of thousands of dollars.

    To address this issue, Nextrope is developing an AI-powered smart contract auditing tool, which:

    Reduces audit costs by automating code analysis.

    Speeds up development cycles by catching vulnerabilities early.

    Improves security by providing quick feedback.

    This AI-powered solution will be a game-changer for the industry, making smart contract development more cost-effective and accessible.

    Conclusion

    Web3 backend development plays a crucial role in scalable and efficient dApps. While full decentralization is ideal in some cases, many projects benefit from a hybrid architecture, where off-chain components optimize performance, reduce costs and improve user experience.

    In future posts in this Web3 backend series, we’ll explore specific implementation details, including:

    How to design a Web3 API for dApps

    Best practices for integrating backend services

    Security challenges and solutions

    Stay tuned for the next article in this series!



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    Stunning Aberdeen Home Listed by Love Pines Realty | Web3Wire

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    Stunning Aberdeen Home Listed by Love Pines Realty | Web3Wire


    385 Shepherd Trail Property Is Now Available for SaleMarch 13, 2025 – Real Estate Agent Jennifer L Carlson is pleased to present 385 Shepherd trail for sale. The adorable modern ranch home is positioned conveniently in Aberdeen on NC 5. Very close to a popular franchise of restaurants and shopping stores. Less than 3 miles from the downtown Main Street in Aberdeen. Approximately six miles to the popular Village of Pinehurst. Close proximity to Fort Bragg Military base… Call Jennifer L Carlson to schedule an appointment to see this home for sale in Aberdeen.

    Image: https://www.abnewswire.com/upload/2025/03/e64eeb43f4d0883416f9ff9ce9348494.jpg

    The home for sale at 385 Shepherd Trail is situated in friendly neighborhood. Excellent curb appeal with the ideal backyard for watching your pup play, or starting your very first garden! This home is just over 1500 square feet. Feels much larger due to the vaulted ceiling in the living room. This home encompasses 3 bedrooms, 2 full baths. The owners suite bathroom offers a sizeable garden tub that could be used to sooth your achy muscles… A glass of wine? With a good book in hand? The home has updated lighting, neutral paint colors, and has been well maintained. The backyard feels incredibly private overlooking wooded green space. In the mornings enjoy a peaceful cup of coffee off the back patio. You will fall in love with this move in ready home in Moore County, North Carolina.

    Open House on Sunday March 16th from 12:00pm – 3:00pm

    Aberdeen, North Carolina the quaint little railroad town “Anchored by a thriving arts community with diverse musical venues, Downtown Aberdeen is now on track as a regional destination for home decor and design, and an uncommon collection of creative entrepreneurs offering specialty retail and services. Downtown Aberdeen is a place of opportunity for all ages.” (http://www.downtownaberdeen.net)

    Image: https://www.abnewswire.com/upload/2025/03/596f2893779e6e2be84a6d7006ac11ee.jpg

    The town of Aberdeen encourages citizens to get involved, open a business, join a board, become part of the community. Military personnel will find they’re close enough to commute to Fort Bragg, but far enough away for their local coffee shop barista to remember their name. Aberdeen is convenient to Uwharrie National Forest, or day trips to the beach or the mountains.

    ‘Love Pines Realty services all areas surrounding Ft Bragg North Carolina, including Southern Pines, Pinehurst, Whispering Pines, Carthage, Aberdeen, West End, Pinebluff, Vass, Cameron, Sanford, Fayetteville, & Raeford.

    For more information, please visit: https://www.lovepines.com

    Media ContactCompany Name: Love Pines RealtyContact Person: Jennifer L Carlson – Owner, Broker, RealtorEmail:Send Email [https://www.abnewswire.com/email_contact_us.php?pr=stunning-aberdeen-home-listed-by-love-pines-realty]City: PinehurstState: North CarolinaCountry: United StatesWebsite: https://www.lovepines.com

    Legal Disclaimer: Information contained on this page is provided by an independent third-party content provider. ABNewswire makes no warranties or responsibility or liability for the accuracy, content, images, videos, licenses, completeness, legality, or reliability of the information contained in this article. If you are affiliated with this article or have any complaints or copyright issues related to this article and would like it to be removed, please contact retract@swscontact.com

    This release was published on openPR.

    About Web3Wire Web3Wire – Information, news, press releases, events and research articles about Web3, Metaverse, Blockchain, Artificial Intelligence, Cryptocurrencies, Decentralized Finance, NFTs and Gaming. Visit Web3Wire for Web3 News and Events, Block3Wire for the latest Blockchain news and Meta3Wire to stay updated with Metaverse News.



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    The Economics of Renting Cloud GPUs: A Comprehensive Breakdown

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    The Economics of Renting Cloud GPUs: A Comprehensive Breakdown


    With global cloud computing spending projected to soar to $1.35 trillion by 2027, businesses and individuals increasingly rely on cloud solutions. Within this landscape, cloud GPUs have become a major area of investment, particularly for AI, machine learning, and high-performance computing (HPC).

    The demand for GPU as a Service (GPUaaS) has fueled a massive market expansion. Valued at $3.23 billion in 2023, the GPUaaS market is expected to reach $49.84 billion by 2032. AI research, deep learning applications, and high-performance computational workloads drive this growth.

    However, is renting cloud GPUs the most cost-effective solution for businesses? Understanding cloud GPUs’ financial implications, use cases, and cost structures is crucial for making informed decisions.

    This article explores the economics of renting cloud GPUs, comparing different pricing models, discussing cost-saving strategies, and analyzing real-world scenarios to help you optimize your cloud computing budget.

    When Should You Rent a Cloud GPU?

    Cloud GPUs provide numerous advantages but are not always the right fit. Before committing to a cloud GPU rental, it’s essential to understand when it makes the most sense. Here are key scenarios where renting a cloud GPU is beneficial:

    1. Short-Term Projects and Peak Demand

    Project-Based Workloads: Renting is more practical than investing in expensive hardware if your project requires high GPU power for a limited time—such as training AI models, rendering 3D animations, or running simulations. If your GPU usage fluctuates, cloud GPUs can scale up when demand is high and down when resources are no longer needed. This eliminates the inefficiency of idle hardware.

    2. Experimentation and Innovation

    Testing New Technologies: Cloud GPUs allow businesses and researchers to experiment with different GPU architectures without incurring large upfront costs. This is crucial for AI research, game development, and other exploratory projects. If you are unsure whether an AI or ML model will be viable, renting cloud GPUs allows you to test your ideas before investing in expensive on-premise infrastructure.

    3. Accessibility and Collaboration

    Democratizing Access to High-Performance GPUs: Not all organizations can afford high-end GPUs. Cloud services provide access to powerful GPU resources for startups, researchers, and developers. With cloud-based GPU computing, team members can work on shared resources, collaborate on machine learning projects, and access data remotely from anywhere.

    4. Reduced IT Overhead

    No Hardware Maintenance: Cloud providers handle GPU maintenance, software updates, and security patches, allowing your team to focus on core tasks. Cloud GPUs eliminate the need for physical data centers, reducing space, cooling systems, and power consumption costs.

    5. Cost-Effectiveness for Specialized Workloads

    Tailored GPU Instances: Many providers offer optimized GPU instances for specific workloads, such as deep learning or scientific computing. These options provide better performance at a lower cost than general-purpose GPUs.

    By analyzing these factors, businesses can determine whether cloud GPU rental is a strategic choice that aligns with their financial and operational goals.

    Understanding the Cost of Renting Cloud GPUs

    Renting a cloud GPU is not just about the hourly rental price—other factors influence the total cost of ownership (TCO), including workload requirements, pricing models, storage, and data transfer fees. Let’s examine the key cost components.

    1. Hourly vs. Reserved Pricing (Including Bare Metal and Clusters)

    On-Demand Instances: Many cloud providers offer pay-as-you-go pricing, which is ideal for short-term projects. For instance, renting an NVIDIA RTX 4090 on Spheron Network (Secure) costs $0.31 / hr. Best for: Users with unpredictable workloads who need flexibility.

    Reserved Instances: Reserved instances can save you 40–60% compared to on-demand pricing, if you require GPUs for extended periods. They are best for Long-term AI model training, HPC workflows, and large-scale simulations.

    Bare Metal Servers: Bare metal servers provide superior performance without virtualization overhead for applications that require dedicated resources and full control. For example, renting a bare metal server with 8 NVIDIA RTX 4090 (Secure) GPUs costs $2.48 /hr and 8 NVIDIA RTX 6000-ADA (Secure) costs $7.20 /hr on Spheron Network. They are best for Real-time AI inference, large-scale rendering, and performance-sensitive applications.

    GPU Clusters: GPU clusters offer high scalability for enterprises conducting parallel processing or large-scale deep learning training. Best for: Distributed AI training and large-scale computational tasks.

    2. Pricing by GPU Type

    Not all GPUs are priced equally. The cost of renting a GPU depends on its capabilities. High-end models like NVIDIA H200 or H100 cost significantly more than older models like the V100 or A4000. Matching the right GPU to your workload is essential to prevent overpaying for unnecessary performance.

    3. Storage and Data Transfer Costs

    Beyond GPU rental, cloud providers charge for:

    Storage: Storing 1TB of training data can cost $5 per month for standard storage, but SSD options cost more.

    Data Transfer Fees: Transferring large datasets between cloud regions can add significant expenses.

    4. Hidden Costs to Watch For

    Assessing your needs and considering scenarios like the one above can help you make smarter decisions about renting cloud GPUs. Let’s look at a real-world example to understand potential costs and how to save money.

    Case Study: Cost Breakdown of AI Model Training

    When planning an AI model training project, the first thought that often comes to mind is: “Let’s do it on‑premise!” In this case study, we’ll walk through the cost breakdown of building an on‑premise system for training AI models. We’ll begin by looking at the more cost‑efficient NVIDIA V100 GPUs.

    Suppose a company needs to train a deep learning model for computer vision. They require 8x NVIDIA V100 GPUs for 30 days. Here’s how the costs:

    On‑Premise Cost Breakdown Using NVIDIA V100 GPUs

    Not every training workload requires the absolute highest-end hardware. For many AI inference and moderate training workloads, an on-premise system with 8x NVIDIA V100 GPUs can be a viable choice. Here’s a breakdown of the estimated costs:

    ComponentEstimated Price (USD)Notes

    8 × NVIDIA V100 GPUs$24,000Approximately $3,000 per GPU (used market)

    Compute (CPUs Cost)$30,000High-performance CPUs for parallel processing

    1TB SSD Storage$1,200High-end NVMe drives

    Motherboard$10,000+Specialized board for multi-GPU configurations

    RAM$10,000 – $18,0002TB+ of high-speed DDR5 RAM (can be lower for some workloads)

    NVSwitch$10,000+Required for NVLink-enabled V100 clusters (higher bandwidth)

    Power Supply$5,000 – $8,000Higher power consumption (~250W per V100)

    Cooling$5,000+More aggressive cooling needed compared to V100 (liquid cooling preferred)

    Chassis$6,000+Specialized high-density GPU chassis

    Networking$2,500+High-bandwidth networking cards (100GbE or faster)

    Software & Licensing$6,000+OS, drivers, and specialized AI software

    Total Cost Estimate$109,700 – $134,700+Higher than L4-based setups due to increased power and cooling needs

    After this high-investment project, the Project can think it can recover the investment. One strategy to recover some of the capital investment for an on‑premise system is to resell the hardware on the aftermarket. However, for AI accelerators, the resale market often only returns a fraction of the original cost. For example, second‑hand NVIDIA GPUs might fetch only 40–60% of their new price, depending on market conditions and the hardware’s condition.

    If the resale value isn’t sufficient—if you’re unable to find buyers at your target price—the hardware could end up sitting idle (or “going to dust”), locking away capital and risking obsolescence.

    These challenges—high upfront costs, rapid depreciation, and idle hardware risk—drive many organizations toward cloud-based AI compute services. To understand this better, let’s compare the cloud compute platforms costs side by side.

    8x NVIDIA V100 GPU Rent Cost Breakdown

    ProviderPrice per Hour (1x V100)Price per Hour (8x V100s)Price per DayPrice per Month (30 Days)

    Google$4.69$37.52$900.48$27,014.40

    Amazon$3.76$30.08$721.92$21,657.60

    CoreWeave$1.02$8.16$195.84$5,875.20

    RunPod$0.23$1.84$44.16$1,324.80

    Spheron$0.10$0.80$19.20$576.00

    Spheron Network remains the most affordable option, being 47x cheaper than Google and 37x cheaper than Amazon for V100 compute. Let’s compare another GPU RTX 4090 rent cost.

    1 x RTX 4090 GPU Rent Cost Breakdown

    Cloud ProviderPrice per HourPrice per DayPrice per Month (720 hrs)

    Lambda Labs~$0.85/hr~$20.40~$612.00

    RunPod (Secure Cloud)~$0.69/hr~$16.56~$496.80

    GPU Mart~$0.57/hr~$13.68~$410.40

    Vast.ai Marketplace~$0.37/hr~$8.88~$266.40

    Together.ai~$0.37/hr~$8.88~$266.40

    RunPod (Community Cloud)~$0.34/hr~$8.16~$244.80

    Spheron Network (Secure)~$0.31/hr~$7.44~$223.20

    Spheron Network (Community)~$0.19/hr~$4.56~$136.80

    Note: Except Spheron Network rates, other platform approximate rates can vary based on configuration (CPU/RAM allocation), region, and pricing model (on‑demand, spot, etc.).

    Spheron Network offers the lowest rate at $0.31/hr(Secure) and $0.19/hr(Community), making it between 38.71% and 77.65% cheaper than the other providers in our list, depending on which you compare it to. Unlike traditional cloud providers, Spheron includes all utility costs (electricity, cooling, maintenance) in its hourly rate—no hidden fees.

    While Big cloud providers offer more flexibility and eliminate the maintenance burden, they aren’t always the most cost-efficient solution. Cloud computing is generally cheaper than an on-premise setup, but it’s not necessarily the optimal choice for all use cases. That’s why we have built Spheron Network.

    After reading the above analysis, you might wonder why Spheron is a more cost-effective option compared to other platforms.

    Spheron is a Decentralized Programmable Compute Network that simplifies how developers and businesses use computing resources. Many people see it as a tool for both AI and Web3 projects, but there is more to it than that. It brings together different types of hardware in one place, so you do not have to juggle multiple accounts or pricing plans.

    Spheron lets you pick from high-end machines that can train large AI models, as well as lower-tier machines that can handle everyday tasks, like testing or proof-of-concept work and deploying SLMs or AI agents. This balanced approach can save time and money, especially for smaller teams that do not need the most expensive GPU every time they run an experiment. Instead of making big claims about market sizes, Spheron focuses on the direct needs of people who want to build smart, efficient, and flexible projects.

    As of this writing, the Community GPUs powered by Spheron Fizz Node are below. Unlike traditional cloud providers, Spheron includes all utility costs in its hourly rate—there are no hidden fees or unexpected charges. You see the exact cost you have to pay, ensuring complete transparency and affordability.

    Spheron’s GPU marketplace is built by the community, for the community, offering a diverse selection of GPUs optimized for AI training, inference, machine learning, 3D rendering, gaming, and other high-performance workloads. From the powerhouse RTX 4090 for intensive deep learning tasks to the budget-friendly GTX 1650 for entry-level AI experiments, Spheron provides a range of compute options at competitive rates.

    By leveraging a decentralized network, Spheron not only lowers costs but also enhances accessibility, allowing individuals and organizations to harness the power of high-end GPUs without the constraints of centralized cloud providers. Whether you’re training large-scale AI models, running Stable Diffusion, or optimizing workloads for inference, Spheron Fizz Node ensures you get the most value for your compute needs.

    High-End / Most Powerful & In-Demand GPUs

    #GPU ModelPrice per Hour ($)Best for Tasks

    1RTX 40900.19AI Inference, Stable Diffusion, LLM Training

    2RTX 4080 SUPER0.11AI Inference, Gaming, Video Rendering

    3RTX 40800.10AI Inference, Gaming, ML Workloads

    4RTX 4070 TI SUPER0.09AI Inference, Image Processing

    5RTX 4070 TI0.08AI Inference, Video Editing

    6RTX 4070 SUPER0.09ML Training, 3D Rendering

    7RTX 40700.07Gaming, AI Inference

    8RTX 4060 TI0.07Gaming, ML Experiments

    9RTX 40600.07Gaming, Basic AI Tasks

    10RTX 40500.06Entry-Level AI, Gaming

    Workstation / AI-Focused GPUs

    #GPU ModelPrice per Hour ($)Best for Tasks

    11RTX 6000 ADA0.90AI Training, LLM Training, HPC

    12A400.13AI Training, 3D Rendering, Deep Learning

    13L40.12AI Inference, Video Encoding

    14P400.09AI Training, ML Workloads

    15V100S0.12Deep Learning, Large Model Training

    16V1000.10AI Training, Cloud Workloads

    High-End Gaming / Enthusiast GPUs

    #GPU ModelPrice per Hour ($)Best for Tasks

    17RTX 3090 TI0.16AI Training, High-End Gaming

    18RTX 30900.15AI Training, 3D Rendering

    19RTX 3080 TI0.09AI Inference, Gaming, Rendering

    20RTX 30800.08AI Inference, Gaming

    21RTX 3070 TI0.08Gaming, AI Inference

    22RTX 30700.07Gaming, Basic AI

    23RTX 3060 TI0.07Gaming, 3D Rendering

    24RTX 30600.06Entry-Level AI, Gaming

    25RTX 3050 TI0.06Basic AI, Gaming

    26RTX 30500.06Basic AI, Entry-Level Workloads

    Older High-End / Mid-Range GPUs

    #GPU ModelPrice per Hour ($)Best for Tasks

    27RTX 2080 TI0.08Gaming, ML, AI Inference

    28RTX 2060 SUPER0.07Gaming, Basic AI Training

    29RTX 20600.06Gaming, AI Experiments

    30RTX 20500.05Entry-Level AI, Gaming

    Entry-Level & Budget GPUs

    #GPU ModelPrice per Hour ($)Best for Tasks

    31GTX 1660 TI0.07Gaming, ML Workloads

    32GTX 1660 SUPER0.07Gaming, ML Workloads

    33GTX 1650 TI0.05Basic AI, Gaming

    34GTX 16500.04Entry-Level AI, Gaming

    Older GPUs with Lower Demand & Power

    #GPU ModelPrice per Hour ($)Best for Tasks

    35GTX 10800.06Gaming, 3D Rendering

    36GTX 1070 TI0.08Gaming, AI Experiments

    37GTX 10600.06Gaming, Entry-Level ML

    38GTX 1050 TI0.07Entry-Level AI, Gaming

    Low-End Workstation GPUs

    #GPU ModelPrice per Hour ($)Best for Tasks

    39RTX 4000 SFF ADA0.16AI Training, Workstation Tasks

    40RTX A40000.09AI Inference, Workstation Workloads

    41T10000.06Entry-Level AI, Graphics Workloads

    Why Choose Spheron Over Traditional Cloud Providers?

    1. Transparent Pricing

    Spheron ensures complete cost transparency with all-inclusive rates. You won’t encounter hidden maintenance or utility fees, making it easier to budget your infrastructure expenses. Traditional cloud providers often impose complex billing structures that lead to unexpected costs, but Spheron eliminates that frustration.

    2. Simplifying Infrastructure Management

    One reason to look at Spheron is that it strips away the complexity of dealing with different providers. If you decide to host a project in the cloud, you often navigate a maze of services, billing structures, and endless documentation. That can slow development and force you to spend energy on system admin work instead of your core product. Spheron reduces that friction. It acts like a single portal where you see your available compute options at a glance. You can filter by cost, power, or any other preference. You can select top-notch hardware for certain tasks and switch to more modest machines to save money. This helps you avoid the waste when you reserve a large machine but only need a fraction of its power.

    3. Optimized for AI Workloads

    Spheron provides high-performance compute tailored for AI, machine learning, and blockchain applications. The platform offers:

    Bare metal servers for intensive workloads.

    Community GPUs for large-scale AI model training.

    Flexible configurations that let users scale resources as needed.

    4. Seamless Deployment

    Spheron removes unnecessary barriers to cloud computing. Unlike traditional cloud services that require lengthy signups, KYC processes, and manual approvals, Spheron lets users deploy instantly. Simply configure your environment and start running workloads without delays.

    5. Blending AI and Web3 Support

    Spheron unifies AI and Web3 by offering a decentralized compute platform that caters to both domains. AI developers can leverage high-performance GPUs for large-scale computations, while Web3 developers benefit from blockchain-integrated infrastructure. This combined approach allows users to run AI models and smart contract-driven applications on a single platform, reducing the need to juggle multiple services.

    6. Resource Flexibility

    Technology evolves rapidly, and investing in hardware can be risky if it becomes outdated too soon. Spheron mitigates this risk by allowing users to switch to new machines as soon as they become available. Whether you need high-powered GPUs for deep learning or cost-effective compute for routine tasks, Spheron provides a marketplace where you can select the best resources in real-time.

    7. Fizz Node: Powering Decentralized Compute at Scale

    Fizz Node is a core component of Spheron’s infrastructure, enabling efficient global distribution of compute power. Fizz Node enhances scalability, redundancy, and reliability by aggregating resources from multiple providers. This decentralized model eliminates the inefficiencies of traditional cloud services and ensures uninterrupted access to compute resources.

    Current Fizz Node Network Statistics:

    10.3K GPUs

    767.4K CPU cores

    35.2K Mac chips

    1.6 PB of RAM

    16.92 PB of storage

    175 unique regions

    These numbers reflect Spheron’s ability to handle high-performance workloads for AI, Web3, and general computing applications globally.

    8. Access to a Wide Range of AI Base Models

    Spheron offers a curated selection of AI Base models, allowing users to choose the best project fit. Available models include:

    All models use BF16 precision, ensuring efficiency and reliability for both small-scale experiments and large-scale computations. The platform presents model details in a clear, intuitive interface, making it easy to compare options and make informed decisions.

    9. User-Friendly Deployment Process

    Spheron prioritizes ease of use by eliminating technical barriers. The platform’s guided setup process includes:

    Define your deployment in YAML: Use a standardized format to specify resources clearly.

    Obtain test ETH: Secure test ETH via a faucet or bridge to the Spheron Chain for deployment costs.

    Explore provider options: Browse available GPUs and regions at provider.spheron.network or fizz.spheron.network.

    Launch your deployment: Click “Start Deployment” and monitor logs in real-time.

    These steps ensure a smooth experience, whether you’re a beginner setting up your first AI Agent or an experienced developer configuring advanced workloads.

    Want to test it out? Just go to the Spheron Awesome repo and https://github.com/spheronFdn/awesome-spheron, which has a collection of ready-to-deploy GPU templates for Spheron.

    10. The Aggregator Advantage

    Spheron operates as an aggregator, pooling resources from multiple providers. This approach enables users to:

    Compare GPU types, memory sizes, and performance tiers in real time.

    Choose from multiple competing providers, ensuring fair pricing.

    Benefit from dynamic pricing, where providers with idle resources lower their rates to attract users.

    This competitive marketplace model prevents price monopolization and provides cost-effective computing options that traditional cloud platforms lack.

    Conclusion

    As you can see, whether you choose on-premise infrastructure or rely on big cloud services, both options come with significant drawbacks. On-premise solutions require massive upfront investments, ongoing maintenance, and scalability challenges, while big cloud providers impose high costs, vendor lock-in, and unpredictable pricing models.

    That’s why Spheron Network is the ideal solution. By leveraging decentralized compute, Spheron provides a cost-effective, scalable, and censorship-resistant alternative. With transparent pricing, high availability, and seamless deployment, Spheron empowers developers, businesses, and AI projects to operate with greater autonomy and efficiency. Choose Spheron and take control of your infrastructure today.



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    Senate Stablecoin Bill Passes Out of Committee With Strong Bi-Partisan Support – Decrypt

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    Senate Stablecoin Bill Passes Out of Committee With Strong Bi-Partisan Support – Decrypt



    The U.S. Senate Banking Committee voted in favor of advancing the stablecoin-focused GENIUS Act to a full Senate vote Thursday, with the legislation receiving bipartisan support. 

    The bill passed through committee by a vote of 18-6, with five Senate Democrats joining Republicans to push the bill over the finish line with considerable breathing room. 

    Democrats who voted for the GENIUS Act’s passage include bill cosponsor Angela Alsobrooks (D-MD), as well as Senate Banking Committee members Mark Warner (D-VA), Andy Kim (D-NJ), Lisa Blunt Rochester (D-DE), and Ruben Gallego (D-AZ). 

    The bill’s sponsor, Bill Hagerty (R-TN), said he intends for the bill to receive a full vote on the Senate floor by the end of April. 

    “The Banking Committee’s strong bipartisan passage of the GENIUS Act out of committee brings us one step closer to providing stablecoin issuers with the choice between state and national charters and will secure our nation’s competitive edge in the rapidly evolving digital asset space,” Sen. Cynthia Lummis (R-WY), another cosponsor of the bill, said in a statement shared with Decrypt. 

    During Thursday’s meeting of the Senate Banking Committee, longtime crypto critic Elizabeth Warren (D-MA) attempted to add multiple new provisions to the GENIUS Act, which creates a legal framework for nonbank stablecoin issuers to participate in the U.S. economy. 

    Warren proposed amendments to the bill that would have blacklisted any stablecoin issuers whose tokens were found to have been used in connection with state enemies and illegal activity, including drug trafficking and the purchase of child pornography. Another would have expanded the provisions of the Act to apply to crypto exchanges and other third parties that interact with stablecoins.

    All her amendments were voted down, mostly along party lines.

    “Who are we trying to protect, the child pornographers and Iran and North Korea?” Warren said at one point, in apparent exasperation, after her third amendment was voted down. 

    “Nobody’s looking to shut this down, no one’s looking to stop innovation,” the progressive senator said at a later point during the meeting. “But we do want to try to make this a little cleaner than it is right now.”

    The bill proceeded shortly after to a vote, which it passed handily.

    A new version of the GENIUS Act was released earlier this week in anticipation of today’s markup. While it has been generally supported by industry leaders, some crypto users pushed back on a new provision that would require stablecoin issuers to have the ability to “seize, freeze, burn, or prevent the transfer” of tokens if obligated to comply with legal orders.

    Edited by James Rubin

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    Binance Secures Landmark $2 Billion Investment from UAE’s MGX – Web3oclock

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    Metaverse Fashion Week Online
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    Binance Secures Landmark Billion Investment from UAE’s MGX – Web3oclock


    MGX , the Abu Dhabi-based investment firm, has announced a $2 billion investment in Binance, the world’s largest crypto exchange. This deal marks the single-largest investment ever made in a cryptocurrency company and Binance’s first-ever institutional investment.

    A Landmark Investment in Digital Finance:

    On March 12, Binance confirmed that MGX, a state-owned UAE investment firm specializing in AI and advanced technology, is acquiring a minority stake in the exchange. The investment, reportedly made in stablecoins, reinforces MGX’s commitment to the future of blockchain and digital finance.

    Ahmed Yahia, CEO of MGX, highlighted the significance of this move:

    “MGX’s investment in Binance reflects our commitment to advancing blockchain’s transformative potential for digital finance.”

    Meanwhile, Binance CEO Richard Teng called the deal a major milestone for both the exchange and the broader cryptocurrency industry:

    “This investment by MGX is a significant milestone for the crypto industry and for Binance. Together, we are shaping the future of digital finance.”

    MGX’s Expansion into Crypto and AI:

    Though MGX had not previously invested in crypto, the firm has been active in the AI and technology sectors. It has previously partnered with OpenAI, SoftBank Group, Oracle, and Microsoft, as well as BlackRock, in a $30 billion AI-focused fund launched in September 2023. This investment represents MGX’s first major venture into blockchain technology and the digital asset industry.

    The UAE government’s proactive approach to crypto regulation and blockchain adoption has positioned the country as a key player in the Middle East & North Africa (MENA) region. According to Chainalysis, the UAE ranks as the third-largest crypto economy in MENA, receiving over $30 billion in crypto transactions between July 2023 and June 2024.

    Binance’s Growing Influence in the UAE:

    As it faces regulatory scrutiny in other jurisdictions, it has expanded significantly in the Middle East. The UAE has become a major hub for the exchange, with 20% of Binance’s global workforce (approximately 1,000 employees) based in the country.

    It continues to dominate the crypto market with over 260 million registered users and more than $100 trillion in cumulative trading volume. Following the MGX investment announcement, its BNB token surged to $563.98, according to CoinMarketCap.

    The Future of Binance and MGX Partnership:

    While the exact stake size MGX acquired remains undisclosed, both companies emphasized that this partnership is designed to drive innovation at the intersection of AI, blockchain, and finance. The investment also signals growing institutional confidence in cryptocurrency as a long-term asset class. Binance CEO Richard Teng is set to discuss the company’s future at CNBC’s CONVERGE LIVE event in Singapore on March 14 at 2:40 p.m. local time (2:40 a.m. ET). Industry watchers anticipate further insights into Binance’s next steps following this historic investment.

    For more insights and updates on Metaverse, DeFi, Blockchain, NFT & Web3, be sure to subscribe to our newsletter. Stay informed on the latest trends and developments in the decentralized world!



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    Google Unveils Gemma 3 – The Ultimate Compact AI Breakthrough – Web3oclock

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    Metaverse Fashion Week Online
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    Google Unveils Gemma 3 – The Ultimate Compact AI Breakthrough – Web3oclock


    Google has introduced Gemma 3, the latest iteration of its lightweight open AI models, built on the same research and technology as its Gemini 2.0 models. Announced in a blog post, Gemma 3 is available in 1B, 4B, 12B, and 27B parameter sizes, offering improved portability, performance, and responsible development.

    Advancing AI Accessibility:

    The Gemma family has seen remarkable adoption, surpassed 100 million downloads and inspired a community-driven ecosystem known as the Gemmaverse, with over 60,000 model variants. With Gemma 3, Google aims to empower developers by providing models that run efficiently on various devices, from phones and laptops to workstations.

    Key Features of Gemma 3:

    State-of-the-Art Performance: Gemma 3 outperforms models like Llama-405B, DeepSeek-V3, and o3-mini, making it one of the best single-accelerator models available.

    Multilingual Capabilities: Supports over 35 languages out-of-the-box and 140+ languages with pretraining.

    Enhanced Reasoning: Advanced capabilities for text, image, and short video analysis enable smarter, more interactive applications.

    Expanded Context Window: With a 128k-token capacity, Gemma 3 can process large amounts of information efficiently.

    Function Calling & Structured Output: Allows automation and agent-based experiences.

    Optimized Performance with Quantization: Official quantized versions reduce computational requirements while maintaining high accuracy.

    Built-In Safety Measures:

    Google emphasizes safety in open AI models, employing rigorous risk assessments and governance measures. Due to its enhanced STEM capabilities, Gemma 3 underwent additional testing to assess potential misuse, with results indicating a low risk level.

    ShieldGemma 2: AI Safety for Images

    Alongside Gemma 3, Google launched ShieldGemma 2, a 4B-parameter AI model designed to enhance image safety. It provides content safety labels for categories such as dangerous content, sexually explicit material, and violence. ShieldGemma 2 is open-source and customizable, giving developers greater flexibility.

    Seamless Integration with Developer Tools:

    Gemma 3 is designed for easy deployment and experimentation:

    Compatible with popular AI frameworks like Hugging Face, PyTorch, JAX, Keras, and Google AI Edge.

    Available on Google AI Studio, Kaggle, and Hugging Face for immediate access.

    Optimized for diverse hardware, including Nvidia GPUs, Google Cloud TPUs, and AMD GPUs.

    Expanding the “Gemmaverse”:

    The Gemmaverse continues to grow, featuring innovations like AI Singapore’s SEA-LION v3 (focused on Southeast Asian languages), INSAIT’s BgGPT (a Bulgarian-first language model), and Nexa AI’s OmniAudio (on-device AI for audio processing).

    To support academic research, Google is launching the Gemma 3 Academic Program, offering $10,000 in Google Cloud credits for selected research projects. Applications are open for four weeks.

    With Gemma 3, Google continues to push the boundaries of accessible, high-performance AI while maintaining a strong focus on safety and responsible development.

    For more insights and updates on Metaverse, DeFi, Blockchain, NFT & Web3, be sure to subscribe to our newsletter. Stay informed on the latest trends and developments in the decentralized world!



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