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March 16, 2025

AMD or NVIDIA? A Complete Guide to Selecting the Right Server GPU

AMD and NVIDIA are the industry titans, each vying for dominance in the high-performance computing market. While both manufacturers aim to deliver exceptional parallel processing capabilities for demanding computational tasks, significant differences exist between their offerings that can substantially impact your server’s performance, cost-efficiency, and compatibility with various workloads. This comprehensive guide explores the nuanced distinctions between AMD and NVIDIA GPUs, providing the insights needed to decide your specific server requirements.

Architectural Foundations: The Building Blocks of Performance

A fundamental difference in GPU architecture lies at the core of the AMD-NVIDIA rivalry. NVIDIA’s proprietary CUDA architecture has been instrumental in cementing the company’s leadership position, particularly in data-intensive applications. This architecture provides substantial performance enhancements for complex computational tasks, offers optimized libraries specifically designed for deep learning applications, demonstrates remarkable adaptability across various High-Performance Computing (HPC) markets, and fosters a developer-friendly environment that has cultivated widespread adoption.

In contrast, AMD bases its GPUs on the RDNA and CDNA architectures. While NVIDIA has leveraged CUDA to establish a formidable presence in the artificial intelligence sector, AMD has mounted a serious challenge with its MI100 and MI200 series. These specialized processors are explicitly engineered for intensive AI workloads and HPC environments, positioning themselves as direct competitors to NVIDIA’s A100 and H100 models. The architectural divergence between these two manufacturers represents more than a technical distinction—it fundamentally shapes their respective products’ performance characteristics and application suitability.

AMD vs NVIDIA: Feature Comparison Chart

FeatureAMDNVIDIA

ArchitectureRDNA (consumer), CDNA (data center)CUDA architecture

Key Data Center GPUsMI100, MI200, MI250XA100, H100

AI AccelerationMatrix CoresTensor Cores

Software EcosystemROCm (open-source)CUDA (proprietary)

ML Framework SupportGrowing support for TensorFlow, PyTorchExtensive, optimized support for all major frameworks

Price PointGenerally more affordablePremium pricing

Performance in AI/MLStrong but behind NVIDIAIndustry-leading

Energy EfficiencyVery good (RDNA 3 uses 6nm process)Excellent (Ampere, Hopper architectures)

Cloud IntegrationAvailable on Microsoft Azure, growingWidespread (AWS, Google Cloud, Azure, Cherry Servers)

Developer CommunityGrowing, especially in open-sourceLarge, well-established

HPC PerformanceExcellent, especially for scientific computingExcellent across all workloads

Double Precision PerformanceStrong with MI seriesStrong with A/H series

Best Use CasesBudget deployments, scientific computing, open-source projectsAI/ML workloads, deep learning, cloud deployments

Software SuiteROCm platformNGC (NVIDIA GPU Cloud)

Software Ecosystem: The Critical Enabler

Hardware’s value cannot be fully realized without robust software support, and here, NVIDIA enjoys a significant advantage. Through years of development, NVIDIA has cultivated an extensive CUDA ecosystem that provides developers with comprehensive tools, libraries, and frameworks. This mature software infrastructure has established NVIDIA as the preferred choice for researchers and commercial developers working on AI and machine learning projects. The out-of-the-box optimization of popular machine learning frameworks like PyTorch for CUDA compatibility further solidified NVIDIA’s dominance in AI/ML.

AMD’s response is its ROCm platform, which represents a compelling alternative for those seeking to avoid proprietary software solutions. This open-source approach provides a viable ecosystem for data analytics and high-performance computing projects, particularly those with less demanding requirements than deep learning applications. While AMD historically has lagged in driver support and overall software maturity, each new release demonstrates significant improvements, gradually narrowing the gap with NVIDIA’s ecosystem.

Performance Metrics: Hardware Acceleration for Specialized Workloads

NVIDIA’s specialized hardware components give it a distinct edge in AI-related tasks. Integrating Tensor Cores in NVIDIA GPUs provides dedicated hardware acceleration for mixed-precision operations, substantially increasing performance in deep learning tasks. For instance, the A100 GPU achieves remarkable performance metrics of up to 312 teraFLOPS in TF32 mode, illustrating the processing power available for complex AI operations.

While AMD doesn’t offer a direct equivalent to NVIDIA’s Tensor Cores, its MI series implements Matrix Cores technology to accelerate AI workloads. The CDNA1 and CDNA2 architectures enable AMD to remain competitive in deep learning projects, with the MI250X chips delivering performance capabilities comparable to NVIDIA’s Tensor Cores. This technological convergence demonstrates AMD’s commitment to closing the performance gap in specialized computing tasks.

Cost Considerations: Balancing Investment and Performance

The premium pricing of NVIDIA’s products reflects the value proposition of their specialized hardware and comprehensive software stack, particularly for AI and ML applications. Including Tensor Cores and the CUDA ecosystem justifies the higher initial investment by potentially reducing long-term project costs through superior processing efficiency for intensive AI workloads.

AMD positions itself as the more budget-friendly option, with significantly lower price points than equivalent NVIDIA models. This cost advantage comes with corresponding performance limitations in the most demanding AI scenarios when measured against NVIDIA’s Ampere architecture and H100 series. However, for general high-performance computing requirements or smaller AI/ML tasks, AMD GPUs represent a cost-effective investment that delivers competitive performance without the premium price tag.

Cloud Integration: Accessibility and Scalability

NVIDIA maintains a larger footprint in cloud environments, making it the preferred choice for developers seeking GPU acceleration for AI and ML projects in distributed computing settings. The company’s NGC (NVIDIA GPU Cloud) provides a comprehensive software suite with pre-configured AI models, deep learning libraries, and frameworks like PyTorch and TensorFlow, creating a differentiated ecosystem for AI/ML development in cloud environments.

Major cloud service providers, including Cherry Servers, Google Cloud, and AWS, have integrated NVIDIA’s GPUs into their offerings. However, AMD has made significant inroads in the cloud computing through strategic partnerships, most notably with Microsoft Azure for its MI series. By emphasizing open-source solutions with its ROCm platform, AMD is cultivating a growing community of open-source developers deploying projects in cloud environments.

Shared Strengths: Where AMD and NVIDIA Converge

Despite their differences, both manufacturers demonstrate notable similarities in several key areas:

Performance per Watt and Energy Efficiency

Energy efficiency is critical for server deployments, where power consumption directly impacts operational costs. AMD and NVIDIA have prioritized improving performance per watt metrics for their GPUs. NVIDIA’s Ampere A100 and Hopper H100 series feature optimized architectures that deliver significant performance gains while reducing power requirements. Meanwhile, AMD’s MI250X demonstrates comparable improvements in performance per watt ratios.

Both companies offer specialized solutions to minimize energy loss and optimize efficiency in large-scale GPU server deployments, where energy costs constitute a substantial portion of operational expenses. For example, AMD’s RDNA 3 architecture utilizes advanced 6nm processes to deliver enhanced performance at lower power consumption compared to previous generations.

Cloud Support and Integration

AMD and NVIDIA have established strategic partnerships with major cloud service providers, recognizing the growing importance of cloud computing for organizations deploying deep learning, scientific computing, and HPC workloads. These collaborations have resulted in the availability of cloud-based GPU resources specifically optimized for computation-intensive tasks.

Both manufacturers provide the hardware and specialized software designed to optimize workloads in cloud environments, creating comprehensive solutions for organizations seeking scalable GPU resources without substantial capital investments in physical infrastructure.

High-Performance Computing Capabilities

AMD and NVIDIA GPUs meet the fundamental requirement for high-performance computing—the ability to process millions of threads in parallel. Both manufacturers offer processors with thousands of cores capable of handling computation-heavy tasks efficiently, along with the necessary memory bandwidth to process large datasets characteristic of HPC projects.

This parallel processing capability positions both AMD and NVIDIA as leaders in integration with high-performance servers, supercomputing systems, and major cloud providers. While different in implementation, their respective architectures achieve similar outcomes in enabling massive parallel computation for scientific and technical applications.

Software Development Support

Both companies have invested heavily in developing libraries and tools that enable developers to maximize the potential of their hardware. NVIDIA provides developers with CUDA and cuDNN for developing and deploying AI/ML applications, while AMD offers machine-learning capabilities through its open-source ROCm platform.

Each manufacturer continually evolves its AI offerings and supports major frameworks such as TensorFlow and PyTorch. This allows them to target high-demand markets in industries dealing with intensive AI workloads, including healthcare, automotive, and financial services.

Choosing the Right GPU for Your Specific Needs

When NVIDIA Takes the Lead

AI and Machine Learning Workloads: NVIDIA’s comprehensive libraries and tools specifically designed for AI and deep learning applications, combined with the performance advantages of Tensor Cores in newer GPU architectures, make it the superior choice for AI/ML tasks. The A100 and H100 models deliver exceptional acceleration for deep learning training operations, offering performance levels that AMD’s counterparts have yet to match consistently.

The deep integration of CUDA with leading machine learning frameworks represents another significant advantage that has contributed to NVIDIA’s dominance in the AI/ML segment. For organizations where AI performance is the primary consideration, NVIDIA typically represents the optimal choice despite the higher investment required.

Cloud Provider Integration: NVIDIA’s hardware innovations and widespread integration with major cloud providers like Google Cloud, AWS, Microsoft Azure, and Cherry Servers have established it as the dominant player in cloud-based GPU solutions for AI/ML projects. Organizations can select from optimized GPU instances powered by NVIDIA technology to train and deploy AI/ML models at scale in cloud environments, benefiting from the established ecosystem and proven performance characteristics.

When AMD Offers Advantages

Budget-Conscious Deployments: AMD’s more cost-effective GPU options make it the primary choice for budget-conscious organizations that require substantial compute resources without corresponding premium pricing. The superior raw computation performance per dollar AMD GPUs offers makes them particularly suitable for large-scale environments where minimizing capital and operational expenditures is crucial.

High-Performance Computing: AMD’s Instinct MI series demonstrates particular optimization for specific workloads in scientific computing, establishing competitive performance against NVIDIA in HPC applications. The strong double-precision floating-point performance of the MI100 and MI200 makes these processors ideal for large-scale scientific tasks at a lower cost than equivalent NVIDIA options.

Open-Source Ecosystem Requirements: Organizations prioritizing open-source software and libraries may find AMD’s approach more aligned with their values and technical requirements. NVIDIA’s proprietary ecosystem, while comprehensive, may not be suitable for users who require the flexibility and customization capabilities associated with open-source solutions.

Conclusion: Making the Informed Choice

The selection between AMD and NVIDIA GPUs for server applications ultimately depends on three primary factors: the specific workload requirements, the available budget, and the preferred software ecosystem. For organizations focused on AI and machine learning applications, particularly those requiring integration with established cloud providers, NVIDIA’s solutions typically offer superior performance and ecosystem support despite the premium pricing.

Conversely, for budget-conscious deployments, scientific computing applications, and scenarios where open-source flexibility is prioritized, AMD presents a compelling alternative that delivers competitive performance at more accessible price points. As both manufacturers continue to innovate and refine their offerings, the competitive landscape will evolve, potentially shifting these recommendations in response to new technological developments.

By carefully evaluating your specific requirements against each manufacturer’s strengths and limitations, you can make an informed decision that optimizes both performance and cost-efficiency for your server GPU implementation, ensuring that your investment delivers maximum value for your particular use case.

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Layer-3s are a necessary innovation in crypto

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March 16, 2025

Layer-3s are a necessary innovation in crypto

The following is a guest post from Rob Viglione, CEO at Horizen Labs.

If we had stopped at dial-up internet, we’d never have gotten Netflix, real-time gaming, or cloud computing. The evolution of internet infrastructure paved the way for mass adoption. In the same way, Layer-3s are an inevitable evolution of blockchain infrastructure—removing friction, lowering costs, and making blockchain truly ready for mainstream users. Yet, critics continue to argue that they add unnecessary complexity.

This debate about the role of Layer-3s is an active one for us at Horizen Labs. The Horizen DAO has recently passed a vote to join the Base ecosystem, a pivotal governance decision that marks the beginning of Horizen’s transition to Base, Coinbase’s Layer 2 network, as an appchain specialized in privacy-preserving applications. We’re convinced by the Layer-3 thesis and believe that Layer 3s represent the next evolution in blockchain scalability.

Horizen’s move to Base isn’t just about following trends, it’s about recognizing that a more modular, interoperable blockchain stack is the key to driving real-world adoption. We’re not just theorizing; we’re building.

The History

For crypto to reach a billion users, transactions need to be fast, cheap, and seamless. Layer-3s aren’t an academic exercise—they’re a practical response to the fact that even Layer-2s aren’t cheap enough for mass adoption. Layer-3s also optimize for special features that are not currently possible on Layer-1s and Layer-2s—such as enhanced ZK capabilities.

Fundamentally, Layer-3s address a core problem: If Ethereum (Layer-1) is expensive, Layer-2s help by processing transactions off-chain and only committing final state proofs to Layer-1. Layer-3s take this further by settling on Layer-2s instead of directly on Ethereum, creating a hierarchical model that minimizes costs at each level.

Layer-3s emerged naturally as blockchain architects sought greater efficiencies. StarkWare first outlined the concept in late 2021 under the term “fractal scaling.” Vitalik Buterin explored Layer-3 designs in 2022, suggesting specialized purposes beyond simple scaling. By 2023, major Ethereum scaling teams began implementing Layer-3 frameworks. Arbitrum introduced Orbit for launching Layer-3 “Orbit chains.” Matter Labs released ZK Stack for building zk-rollups as either Layer-2s or Layer-3s. These developments have pushed Layer-3s from theory to practice.

Not Everyone Is a Fan

Critics argue several points against Layer-3s: many believe Layer-2 solutions haven’t reached full maturity yet, and making Layer-3s is premature. Some argue Layer-3s add complexity. But great technology is about making complexity invisible to users—just like the internet did. Some view Layer-3s as redundant, arguing their goals could be achieved by optimizing Layer-2 solutions.

However, a crucial realization is emerging that makes Layer-3s even more timely: even Layer-2s, built to enable faster, cheaper transactions, might still fall short.

In some cases, a Layer-3 can abstract costs even further, ensuring near-zero gas fees. This cost abstraction is vital. Blockchain adoption requires transactions that are nearly free to the end user, and Layer-3s provide precisely this capability.

That brings a chain-abstracted future closer. Ultimately, that is better for onboarding new users, better for liquidity, and better for incentivizing the building of new dApps onchain. When users can transact without worrying about gas fees, adoption accelerates. Developers can build applications that wouldn’t be economically viable on higher-fee networks, and liquidity flows more freely when not constrained by transaction costs. The entire ecosystem benefits.

But abstraction isn’t just about cost savings; it’s also about usability and customization.

Customization and Connectivity

Layer-3s are also a natural response to the fear of ecosystem isolation. Chains don’t want to be siloed. Standalone Layer-1 blockchains face significant challenges: they must bootstrap their own security, attract users from scratch, and build an entirely new infrastructure. Many “Ethereum killers” like Cardano, Fantom, or Tezos have discovered how difficult this journey can be.

Layer-3s offer an alternative path where chains can remain connected to established ecosystems while providing better customization options: this is where their true potential lies. Application-specific chains can optimize for their unique use cases, whether it’s zero-knowledge proofs, gaming, DeFi, social networks, or enterprise applications. They can implement custom virtual machines, consensus mechanisms, or privacy features tailored to their needs, all while staying connected to the broader ecosystem, benefiting from its liquidity and security.

This blend of customization and connectivity makes these application-specific apps excel at what they do, ultimately benefiting the end users.

A Pathway to Abstraction

People may claim that Layer-3s make web3 too complicated, but there’s a good chance that it could solve its own problem. The complexity will be invisible to end users if implemented correctly.

Modern dApps can abstract away the underlying layers through smart wallet designs and intuitive interfaces. Users needn’t know which layer they’re transacting on any more than internet users need to understand TCP/IP protocols. They simply experience faster, cheaper transactions, and better products.

This natural evolution in blockchain architecture is a positive step. Layer-3s balance sovereignty with interoperability. They maximize cost efficiency without sacrificing security. They enable specialized optimization while maintaining ecosystem connections. These aren’t just nice-to-have features. They’re essential for blockchains to achieve mainstream adoption.

The internet didn’t take off because users understood packet-switching or HTTP protocols. It took off because it just worked. Layer-3s bring us closer to a blockchain world that ‘just works’—seamless, fast, and cost-effective. And that’s how crypto wins.

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

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March 15, 2025

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

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.

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A weekly AI journey narrated by Gen, a generative AI model.

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

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March 15, 2025

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.

BREAKING: The ECB’s Digital Euro is set to launch in October. Key concerns include:

– Real-time transaction tracking– Potential for payment blocking– Automatic tax deductions– Restrictions on cash withdrawals– Programmable money with expiration dates

They couldn’t… pic.twitter.com/UvQH750gbl

— Dr. Simon Goddek (@goddeketal) March 9, 2025

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.

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.

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

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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 competitive landscape of a market explains strategies incorporated by key players of the Data Mesh Market. Key developments and shifts in management in recent years by players have been explained through company profiling. This helps readers to understand the trends that will accelerate the growth of the Data Mesh Market. It also includes investment strategies, marketing strategies, and product development plans adopted by major players of the Data Mesh Market. The market forecast will help readers make better investments.

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

SolutionsServices

By Deployment Mode

On-PremisesCloud

By Organization Size

Large EnterprisesSmall & Medium Enterprises (SMEs)

By Industry Vertical

BFSIHealthcareRetail & E-commerceIT & TelecomManufacturingGovernmentOthers

By Region

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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The regional segmentation study is one of the best offerings of the report that explains why some regions are taking the lead in the Data Mesh Market while others are making a low contribution to the global market growth. Each regional market is comprehensively researched in the report with accurate predictions about its future growth potential, market share, market size, and market growth rate.

Geographic Segment Covered in the Report:

• 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)

Key questions answered in the report:

• What is the growth potential of the Data Mesh Market?• Which product segment will take the lion’s share?• Which regional market will emerge as a pioneer in the years to come?• Which application segment will experience strong growth?• What growth opportunities might arise in the Welding industry in the years to come?• What are the most significant challenges that the Data Mesh Market could face in the future?• Who are the leading companies on the Data Mesh Market?• What are the main trends that are positively impacting the growth of the market?• What growth strategies are the players considering to stay in the Data Mesh Market?

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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.

pectra is shaping up to a BIG upgrade, possibly the largest in ethereum’s history, so let’s review all the code changes that are going into pectra so far and what is still TBD:

— Christine Kim (@christine_dkim) June 6, 2024

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.

ACDE Recap

A new testnet, Hoodi, is going live Monday to wrap up Pectra testing, if you need to test validator exits, be on the lookout for it! Everything else can be tested on Sepolia & Holesky.

Pectra will be scheduled 30+ days after Hoodi forks successfully, pending infra… pic.twitter.com/FK05GmtPnk

— timbeiko.eth (@TimBeiko) March 13, 2025

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.

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.

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The Economics of Renting Cloud GPUs: A Comprehensive Breakdown

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March 13, 2025

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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March 13, 2025

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.

A massive move in Washington today. The GENUIS Act passed out of Senate Banking with a strong and bi-partisan 18-6 vote. This is a huge step towards providing regulatory clarity for stablecoins, and a huge step towards upgrading and making the dollar more competitive.

— Jeremy Allaire – jda.eth / jdallaire.sol (@jerallaire) March 13, 2025

“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.

THANK YOU to moderate Democrats–the stablecoin bill (GENIUS Act) passed the Senate Banking Committee 18-6. They bucked the far-left Warrenites.

— Caitlin Long (@CaitlinLong_) March 13, 2025

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.

It turns out the anti-crypto army is neither anti-crypto nor much of an army

— paulgrewal.eth (@iampaulgrewal) March 13, 2025

“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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