#GPU procurement

[currentmediasstuff]

India AI Mission: Union Minister Ashwini Vaishnaw Unveils Rs 10,000 Crore Initiative

Union IT Minister Ashwini Vaishnaw announced on Wednesday the launch of the Rs 10,000 crore India AI Mission, slated to roll out in the next two to three months. This ambitious initiative aims to bolster India’s capabilities in artificial intelligence (AI) by procuring computing power, fostering innovation, and enhancing skill development across the industry.

Key Components of India AI Mission

During the inaugural session of the Global IndiaAI Summit 2024, Minister Vaishnaw outlined several key components of the India AI Mission:

1.Graphics Processing Units (GPUs) Procurement: The government plans to procure 10,000 or more GPUs under a public-private partnership. This initiative aims to enhance industry efficiencies and support larger-scale AI applications.

2.AI Innovation Centre: The mission will establish an AI innovation centre to foster cutting-edge research and development in AI technologies.

3.High-Quality Data Sets: Special emphasis will be placed on creating high-quality data sets that can add significant value to startup initiatives and AI development efforts.

4.Application Development Initiative: A dedicated effort will be made to develop applications that address socio-economic challenges faced by India, leveraging AI solutions.

5.Focus on Skill Development: The India AI Mission will prioritize skill development initiatives to equip the workforce with the necessary capabilities to harness AI technologies effectively.

Strategic Importance and Global Context

Vaishnaw highlighted the strategic importance of making modern technology accessible to all, citing India’s digital public infrastructure as a prime example. He underscored the need for equitable access to technology, contrasting it with global trends where technology often remains limited to a few dominant entities.

Addressing AI Challenges

Acknowledging the dual nature of AI’s impact, Vaishnaw discussed the opportunities and challenges posed by AI technologies. He noted the global efforts to regulate AI, such as the AI Act in the European Union and executive orders in the United States, emphasizing their relevance in addressing AI-related issues globally.

Conclusion

As AI continues to evolve and impact various sectors, Minister Vaishnaw emphasized the importance of balancing innovation with responsible use. The India AI Mission, with its comprehensive approach to fostering AI capabilities, aims to position India as a leader in AI innovation while addressing societal challenges and ensuring inclusive growth.

[darkmaga-retard]

News Items Weekend Edition.

John Ellis

Sep 14, 2024

1. A regulatory dispute in Ohio may help answer one of the toughest questions hanging over the nation’s power grid: Who will pay for the huge upgrades needed to meet soaring energy demand from the data centers powering the modern internet and artificial intelligence revolution? Google, Amazon, Microsoft and Meta are fighting a proposal by an Ohio power company to significantly increase the upfront energy costs they’ll pay for their data centers, a move the companies dubbed “unfair” and “discriminatory” in documents filed with Ohio’s Public Utility Commission last month. American Electric Power Ohio said in filings that the tariff increase was needed to prevent new infrastructure costs from being passed on to other customers such as households and businesses if the tech industry should fail to follow through on its ambitious, energy-intensive plans. The case could set a national precedent that helps determine whether and how other states force tech firms to be accountable for the costs of their growing energy consumption. (Source: washingtonpost.com)

2. Although renewable energy is attracting more investment worldwide, a significant bottleneck has emerged: inadequate power grids. One estimate suggests that solar and wind facilities capable of generating electricity equivalent to 480 nuclear reactors remain unconnected to transmission networks in the U.S. and Europe. In Asia, the South Korean government rejected U.S. asset manager BlackRock's application to build an offshore wind farm in January, citing a lack of available grid capacity, according to local media. Last year, the world added about 560 gigawatts of renewable power capacity, a 64% increase from 2022, according to the International Energy Agency (IEA). However, many of these projects lack grid access because they are located in areas without thermal or other power plants, and the expansion of grid infrastructure has not kept pace with the rapid growth of renewable plants. (Source: asia.nikkei.com)

3. Leopold Aschenbrenne:

You can see the future first in San Francisco.  Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.

[govindhtech]

Red Hat RHEL AI: AI Innovation Rises With Dell & Red Hat

Red Hat RHEL AI

For more than 20 years, Dell Technologies and Red Hat RHEL AI have worked together to provide reliable, enterprise-grade solutions. With the announcement this week that the Dell PowerEdge 760xa is the first server platform verified and supported for Red Hat Enterprise Linux AI (RHEL AI), the two businesses accomplished another significant milestone in their long-standing partnership.

With the help of Dell most recent partnership, businesses of all sizes can easily adopt AI and realize its full potential.

Using AI to Produce Transformative Results

AI has transformed the industry and is no longer futuristic. Generative AI is changing software development, content creation, and business decisions. All industries are using AI to improve customer experiences, automate operations, and analyze vast datasets. Here are some instances of AI in action:

Healthcare: AI-driven diagnosis and individualized treatment programs.

Money: Predictive analytics for risk management and fraud detection.

Shop: Improved client experiences with tailored suggestions.

Producing: predictive upkeep as well as process improvement.

AI has a huge potential for future innovation. Companies that make AI technology investments now will be better positioned to gain a competitive edge later on.

Unleashing AI Innovation with Dell PowerEdge and RHEL AI

Enterprises may benefit from the unmatched advantages that come with making AI accessible and expediting the deployment of Dell PowerEdge servers with Red Hat RHEL AI. With the solution, developers, application owners, and domain experts can create AI-powered apps more quickly and with enterprise-grade results.

What is RHEL AI?

Red Hat RHEL AI is a cutting-edge AI platform that assembles essential parts to begin quickly and effectively developing and executing AI models and applications. It blends

The Granite LLM family: Large language models (LLMs) distributed under Apache-2.0 license that are licensed under an open-source license.

Tools for aligning models in InstructLab: Tools that are simple to use and intuitive that improve LLM capabilities while opening up AI model creation to a wider audience.

RHEL bootable image: Granite models and the InstructLab toolchain, designed for hardware GPU accelerators, are supplied as a bootable RHEL image along with Pytorch/runtime libraries.

Business assistance and indemnity: Red Hat offers model intellectual property indemnity as well as technical support.

RHEL AI

RHEL AI Optimization for Dell PowerEdge

With RHEL AI, the Dell PowerEdge R760xa offers a strong platform for developing and executing resource-intensive AI applications.

Server platform validation came first: For Red Hat RHEL AI, Dell PowerEdge is the first server platform that has been verified, enhanced, and supported, guaranteeing peak performance and consistent results.

Strong and adaptable: In a 2U PowerEdge R760xa node, the system can accommodate up to 4X NVIDIA H100 or 4X NVIDIA L40S GPUs, making it suitable for GPU-intensive tasks such as inferencing, model training, and advanced analytics.

Procurement from a single source: The solution makes it easier for businesses to purchase and receive assistance, enabling them to purchase the complete solution from Dell. In addition, Dell offers Red Hat first-call assistance, recognizing and prioritizing issues that pertain to Red Hat RHEL AI components.

Enabling AI Accessibility on Dell PowerEdge with RHEL AI

While reducing possible risks with a validated, supported platform, Red Hat RHEL AI on Dell PowerEdge assists developers, app owners, domain experts, and data scientists in streamlining the creation and operation of their AI-centric apps. Many users may create AI applications without relying on scarce data scientists because to the solution’s InstructLab tooling and Granite models.

AI demands a lot of resources, including GPUs, compute power, and servers that are capable of handling it. It is crucial that businesses build on a platform that can grow with them and give them the flexibility to experiment and create AI-driven innovations as they assess and apply GenAI use cases.

This platform may make your AI journey easier, whether you’re creating and optimizing AI models, incorporating AI into already-existing apps, or creating brand-new AI-driven solutions.

Dell PowerEdge servers

Raise the Bar for AI with Red Hat and Dell

Red Hat Enterprise Linux AI on Dell PowerEdge servers allows companies to streamline and expedite their AI implementation. This potent combination provides a verified, enhanced, and backed platform intended to reduce obstacles and quicken innovation.

Read more on govindhtech.com

[jcmarchi]

The exponential expenses of AI development

New Post has been published on https://thedigitalinsider.com/the-exponential-expenses-of-ai-development/

The exponential expenses of AI development

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Tech giants like Microsoft, Alphabet, and Meta are riding high on a wave of revenue from AI-driven cloud services, yet simultaneously drowning in the substantial costs of pushing AI’s boundaries. Recent financial reports paint a picture of a double-edged sword: on one side, impressive gains; on the other, staggering expenses. 

This dichotomy has led Bloomberg to aptly dub AI development a “huge money pit,” highlighting the complex economic reality behind today’s AI revolution. At the heart of this financial problem lies a relentless push for bigger, more sophisticated AI models. The quest for artificial general intelligence (AGI) has led companies to develop increasingly complex systems, exemplified by large language models like GPT-4. These models require vast computational power, driving up hardware costs to unprecedented levels.

To top it off, the demand for specialised AI chips, mainly graphics processing units (GPUs), has skyrocketed. Nvidia, the leading manufacturer in this space, has seen its market value soar as tech companies scramble to secure these essential components. Its H100 graphics chip, the gold standard for training AI models, has sold for an estimated $30,000 — with some resellers offering them for multiple times that amount. 

The global chip shortage has only exacerbated this issue, with some firms waiting months to acquire the necessary hardware. Meta Chief Executive Officer Zuckerberg previously said that his company planned to acquire 350,000 H100 chips by the end of this year to support its AI research efforts. Even if he gets a bulk-buying discount, that quickly adds to billions of dollars.

On the other hand, the push for more advanced AI has also sparked an arms race in chip design. Companies like Google and Amazon invest heavily in developing their AI-specific processors, aiming to gain a competitive edge and reduce reliance on third-party suppliers. This trend towards custom silicon adds another layer of complexity and cost to the AI development process.

But the hardware challenge extends beyond just procuring chips. The scale of modern AI models necessitates massive data centres, which come with their technological hurdles. These facilities must be designed to handle extreme computational loads while managing heat dissipation and energy consumption efficiently. As models grow larger, so do the power requirements, significantly increasing operational costs and environmental impact.

In a podcast interview in early April, Dario Amodei, the chief executive officer of OpenAI-rival Anthropic, said the current crop of AI models on the market cost around $100 million to train. “The models that are in training now and that will come out at various times later this year or early next year are closer in cost to $1 billion,” he said. “And then I think in 2025 and 2026, we’ll get more towards $5 or $10 billion.”

Then, there is data, the lifeblood of AI systems, presenting its own technological challenges. The need for vast, high-quality datasets has led companies to invest heavily in data collection, cleaning, and annotation technologies. Some firms are developing sophisticated synthetic data generation tools to supplement real-world data, further driving up research and development costs.

The rapid pace of AI innovation also means that infrastructure and tools quickly become obsolete. Companies must continuously upgrade their systems and retrain their models to stay competitive, creating a constant cycle of investment and obsolescence.

“On April 25, Microsoft said it spent $14 billion on capital expenditures in the most recent quarter and expects those costs to “increase materially,” driven partly by AI infrastructure investments. That was a 79% increase from the year-earlier quarter. Alphabet said it spent $12 billion during the quarter, a 91% increase from a year earlier, and expects the rest of the year to be “at or above” that level as it focuses on AI opportunities,” the article by Bloomberg reads.

Bloomberg also noted that Meta, meanwhile, raised its estimates for investments for the year and now believes capital expenditures will be $35 billion to $40 billion, which would be a 42% increase at the high end of the range. “It cited aggressive investment in AI research and product development,” Bloomberg wrote.

Interestingly, Bloomberg’s article also points out that despite these enormous costs, tech giants are proving that AI can be a real revenue driver. Microsoft and Alphabet reported significant growth in their cloud businesses, mainly attributed to increased demand for AI services. This suggests that while the initial investment in AI technology is staggering, the potential returns are compelling enough to justify the expense.

However, the high costs of AI development raise concerns about market concentration. As noted in the article, the expenses associated with cutting-edge AI research may limit innovation to a handful of well-funded companies, potentially stifling competition and diversity in the field. Looking ahead, the industry is focusing on developing more efficient AI technologies to address these cost challenges. 

Research into techniques like few-shot learning, transfer learning, and more energy-efficient model architectures aims to reduce the computational resources required for AI development and deployment. Moreover, the push towards edge AI – running AI models on local devices rather than in the cloud – could help distribute computational loads and reduce the strain on centralised data centres. 

This shift, however, requires its own set of technological innovations in chip design and software optimisation. Overall, it is clear that the future of AI will be shaped not just by breakthroughs in algorithms and model design but also by our ability to overcome the immense technological and financial hurdles that come with scaling AI systems. Companies that can navigate these challenges effectively will likely emerge as the leaders in the next phase of the AI revolution.

(Image by Igor Omilaev)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

Tags: ai, artificial intelligence

[ricardojordao9]

Critérios para Selecionar o Notebook Ideal para Programação

Escolher o notebook ideal para programação e desenvolvimento pode ser uma tarefa desafiadora, especialmente com tantas opções no mercado. Você precisa de um equipamento que ofereça desempenho, durabilidade e conforto para longas horas de trabalho. Neste artigo, vamos explorar os principais critérios para selecionar o melhor notebook para suas necessidades, incluindo comparações como asus ou lenovo e outros fatores essenciais.

Entendendo Suas Necessidades

Antes de mergulhar nas especificações técnicas, é importante entender suas necessidades específicas. Você é um desenvolvedor front-end, back-end ou full-stack? Trabalha com aplicações pesadas como machine learning e big data, ou foca mais em desenvolvimento web e mobile? Essas perguntas ajudam a definir o que procurar em um notebook ideal para trabalho.

Processador e Memória RAM

O processador é o coração do seu notebook. Para desenvolvimento, recomendamos pelo menos um Intel Core i5 ou Ryzen 5. Se você trabalha com tarefas mais intensivas, como compilar grandes projetos ou rodar máquinas virtuais, um Intel Core i7 ou Ryzen 7 é ideal. A memória RAM também é crucial; 8GB é o mínimo recomendável, mas 16GB ou mais é o ideal para garantir que você possa trabalhar sem problemas de desempenho.

Armazenamento

O tipo e a quantidade de armazenamento também são fundamentais. Um SSD (Solid State Drive) é preferível a um HDD, pois oferece tempos de leitura e gravação muito mais rápidos. Isso resulta em um sistema mais responsivo e tempos de compilação reduzidos. Um SSD de 256GB é um bom começo, mas 512GB ou mais é ideal, especialmente se você trabalha com muitos projetos ou arquivos grandes.

Tela e Resolução

A tela do seu notebook é onde você passará a maior parte do tempo, então escolher uma de boa qualidade é essencial. Uma resolução Full HD (1920x1080) é o mínimo recomendável para proporcionar clareza e espaço suficiente para múltiplas janelas de código. Telas maiores de 15 polegadas são preferíveis, mas um modelo de 13 ou 14 polegadas pode ser mais portátil.

Teclado e Conforto

Programadores passam muitas horas digitando, então um teclado confortável é essencial. Procure um notebook com um teclado de tamanho completo, teclas bem espaçadas e um bom feedback tátil. A retroiluminação do teclado também pode ser um diferencial importante se você trabalha em ambientes com pouca luz.

Portabilidade

A portabilidade é uma consideração importante, especialmente se você trabalha remotamente ou viaja frequentemente. Notebooks leves e finos são mais fáceis de transportar, mas podem sacrificar desempenho. Encontrar um equilíbrio entre potência e portabilidade é crucial. Modelos como os ultrabooks são ideais para esse perfil de usuário.

Bateria

A duração da bateria é um fator crucial para quem precisa de mobilidade. Procure notebooks que ofereçam pelo menos 8 horas de autonomia para garantir que você possa trabalhar ou jogar sem precisar de uma tomada o tempo todo. Alguns modelos podem oferecer até 12 horas de uso com uma única carga.

Considerações Gráficas

Embora a maioria dos desenvolvedores não precise de uma placa gráfica dedicada, se você trabalha com desenvolvimento de jogos ou edição de vídeo, uma GPU dedicada pode ser essencial. Modelos como o NVIDIA GeForce ou AMD Radeon são boas opções.

Comparações de Marcas

Escolher entre marcas como asus ou lenovo pode ser difícil. Ambas oferecem modelos robustos e confiáveis, mas a decisão final pode depender de preferências pessoais e características específicas de cada modelo. Leve em consideração também outras marcas como Dell, HP e Apple, que têm modelos excelentes para desenvolvedores.

Exemplos de Notebooks

Dell XPS 15 - Excelente desempenho com opções de processadores i7 e i9, além de uma tela 4K.

MacBook Pro 16" - Ótimo para desenvolvedores iOS e macOS, com desempenho robusto e tela Retina.

Lenovo ThinkPad X1 Carbon - Portátil, durável e com um teclado muito confortável.

Asus ZenBook Pro Duo - Inovador com uma segunda tela para multitarefa.

Escolher o notebook certo para programação e desenvolvimento envolve considerar uma variedade de fatores, desde o processador e memória RAM até a portabilidade e duração da bateria. Avalie suas necessidades e orçamento para encontrar o modelo que melhor se adapte ao seu trabalho. E não se esqueça de verificar opções específicas para outras atividades, como notebook para estudar e notebook perfeito para jogos.

Ao final, seu objetivo é encontrar um equilíbrio entre desempenho, conforto e preço. Seja você um programador iniciante ou um desenvolvedor experiente, o notebook certo pode fazer uma grande diferença na sua produtividade e satisfação no trabalho. Boa sorte na sua escolha!

[jhavelikes]

You can see the future first in San Francisco. Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum. The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace many college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be unleashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war. Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the willful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.

Introduction - SITUATIONAL AWARENESS: The Decade Ahead

[topreviewin]

Apple is expected to exercise loads of billion on hardware to enhance its artificial intelligence style in 2024, in accordance with speculation from Apple analyst Ming-Chi Kuo. Kuo expects Apple to exercise "at the least" $620 million on servers in 2023 and $4.75 billion on servers in 2024. Apple may maybe maybe contain between 2,000 and 3,000 servers this yr, and as much as twenty,000 next yr. Kuo thinks that Apple is purchasing servers outfitted with Nvidia's HGX H100 8-GPU for generative AI coaching, with the corporate planning to upgrade to B100 next yr. Nvidia calls its H100 an AI supercomputing platform, and each is priced at around $250,000. Kuo looks guessing at Apple's purchasing plans here, and he says that he expects Apple will exercise AI servers it's a long way purchasing and putting in itself to put together astronomical language objects in site of digital internet webhosting from assorted cloud provider companies for improved security and privateness. He does boom that Apple may maybe maybe way its enjoy server chips to assign on server costs, nonetheless he has considered no evidence that Apple is doing that right this moment. While Apple looks making a prime investment into AI, Apple's server purchasing will drop at the wait on of assorted corporations love Meta and Microsoft. Apple may maybe also procure to put money into labor costs, infrastructure, and additional, and Kuo suggests that Apple must exercise loads of billion bucks each yr to procure a broad gamble of catching up with competitors. Kuo claims that he's "in actuality concerned" relating to the vogue forward for Apple's generative AI enterprise if Apple spends only one billion bucks a yr as advised by Bloomberg's Designate Gurman. Over the weekend, Gurman mentioned that Apple is heading within the reliable route to exercise $1 billion per yr on its AI efforts. Gurman says that Apple is engaged on a brand novel, smarter model of Siri and is aiming to combine AI into many Apple apps. Current Reports iOS 17.1 On hand Subsequent Week With These 8 Recent Components for iPhones iOS 17.1 is expected to be released by Tuesday, October 24 following weeks of beta testing. The tool replace entails loads of novel parts and adjustments for iPhones connected to Apple Music, AirDrop, StandBy mode, and additional. Beneath, we now procure got recapped eight novel parts and adjustments coming to the iPhone with iOS 17.1. When the tool replace is on hand, users would maybe be ready to set up it by... Gurman: Apple to Train Recent Macs This Month Apple is planning a Mac-targeted product delivery, likely together with the announcement of a refreshed 24-hunch iMac, for the head of this month, Bloomberg's Designate Gurman stories. Subscribe to the MacRumors YouTube channel for extra movies. Within the most traditional edition of his "Energy On" e-newsletter, Gurman mentioned that Apple is "planning a Mac-centered product delivery around the head of this month" that would watch... iOS 17.1 At possibility of Initiate Tomorrow Apple's iOS 17.1, iPadOS 17.1, macOS Sonoma 14.1, tvOS 17.1, watchOS 10.1, and HomePod Software 17.1 updates are expected to be released to the general public the following day following loads of weeks of beta testing. We're looking ahead to the tool to head reside at 10:00 a.m. Pacific Time, which is when Apple assuredly releases updates. Remaining week, Apple seeded liberate candidates (RCs) for all of the upcoming... Fingers-On With the $1,700 'OnePlus Originate' Foldable Smartphone Chinese smartphone company OnePlus this week introduced its first foldable smartphone, the OnePlus Originate. OnePlus joins loads of assorted producers that procure come out with foldable smartphones, together with Samsung, Google, and Xiaomi. We picked up the OnePlus Originate to look at how it compares to Apple's most traditional flagship, the iPhone 15 Professional Max. Subscribe to the MacRumors YouTube channel for extra movies. ... Unreleased

HomePod With LCD Display Allegedly Proven in Shots [Update] Change: Kosutami has since published that the LcdUTest app shown on the HomePod's elevated veil panel is faked, nonetheless the HomePod within the checklist is no longer. Apple is rumored to be increasing a brand novel HomePod with a veil, and novel photos shared on-line allegedly give us a first gape of the novel dapper speaker. The above checklist seems to veil a HomePod similar in size to the second-technology... Top Reports: Recent USB-C Apple Pencil, iPad and iMac Rumors, and Extra After a flurry of rumors suggesting we would watch some novel iPad objects this week, a brand novel Apple Pencil grew to turn into out to the truth is be what became within the playing cards. The novel Apple Pencil costs by technique of USB-C, launches early next month, and is the most payment-efficient mannequin in a lineup that now entails three assorted Apple Pencils. This week saw some extra rumors about future iPad and iMac objects, while we're... Apple Rumored to Adjust to ChatGPT With Generative AI Components on iPhone as Soon as iOS 18 Apple plans to launch imposing generative AI technology on the iPhone and iPad in late 2024 at the earliest, in accordance with Jeff Pu, an analyst who covers Apple's provide chain for Hong Kong-basically based investment firm Haitong Global Securities. In a study reward on Wednesday, Pu mentioned his provide chain assessments imply that Apple is at possibility of create about a hundred AI servers in 2023, and... Bloomberg: Apple At possibility of Care for Mac Initiate Occasion on October 30 or 31 Apple is likely planning a Mac-connected delivery tournament this month that can happen on either Monday, October 30 or Tuesday, October 31, in accordance with Bloomberg's Designate Gurman. Subscribe to the MacRumors YouTube channel for extra movies. The effectively-connected reporter's prediction is in accordance to records purchased from sources with obvious records of Apple's plans, as effectively because the fact that some...

[techrobot1235]

How new AI demands are fueling the data center industry in the post-cloud era

The potential of AI in the data center industry during the post-cloud era

The growing use of artificial intelligence (AI) indicates a significant increase in data demand and a new era of possible data center business development over the next two years and beyond.

After a decade of industry expansion fueled by cloud and mobile platforms, the “AI Era” comes to an end with this transformation. Over the past decade, the top public cloud service providers and internet content providers pushed data center capacity expansion to a world level, culminating in a frenzy of activity from 2020 to 2022 due to an increase in online service consumption and low-interest-rate project financing.

Nonetheless, substantial changes have occurred in the sector over the last year, including a rise in finance prices, project costs, and development delays, as well as acute power limitations in core regions. In many worldwide areas, for example, standard greenfield data center development timeframes have increased to four or more years, approximately twice as long as a few years ago when electricity and land were less restrictive.

Big internet companies are racing to secure data center capacity in important locations while balancing AI potential and concerns. The instability and uncertainty will raise the level of risk and make navigating the industry more difficult.

The automated procurement of data center capacity has come to an end

Cloud service companies enhanced demand forecasting and automated capacity buying throughout the Cloud Era. They had to return for extra capacity since demand surpassed expectations. Customers’ willingness to accept larger deals and lease capacity at higher costs has risen over the last two years, especially in areas with more available power.

Expansion of Self-build data center building strategies

For efficient market access, hyperscale purchasers in the data centre business are adjusting their self-build approach to rely on leased capacity from third parties. They recognise that self-building is unfeasible and are proposing smaller self-builds to meet future demand. This transition may result in a more diversified mix of self-built and leased capacity, necessitating the assessment of possible migration risks by third-party providers.

Increasing power demand for AI workloads, liquid cooling

AI workloads need high power density in data centres owing to the use of GPUs. Nvidia controls 95% of the GPU market for machine learning, resulting in high-end AI workloads operating on comparable technology. This leads to rack densities of 30-40kW, compared to 10kW/rack for public cloud applications. To solve this, hyperscalers and data center operators are focused on effective cooling systems, with some large hyperscalers proposing to move to liquid cooling solutions or increasing data datacrentre temperatures.

ESG (Environmental, Social, and Governance) standards

The industry of data centres The primary emphasis of ESG concerns is sustainability. The data center business stresses sustainability via renewable energy, water consumption, and carbon footprint reduction, employing a variety of ways to accomplish these objectives.

Enhancements to Efficiency

Energy-efficient designs such as free cooling, efficient power distribution, and efficient lighting systems must be recommended.

1. Use of renewable energy

Using the grid to obtain renewable energy.

Solar and wind are the best renewable resources.

Power purchase agreements (PPAs) specify the volume and price of long-term renewable energy.

2. Water consumption

Systems that are cooled by air.

Closed-loop water systems can reduce water consumption.

Water recycling and rainwater harvesting can reduce water consumption.

Waterless cooling technologies, like evaporative or adiabatic cooling, can assist in cooling systems.

3. Carbon balance

The heat from IT equipment is used to recover energy.

4. Waste minimization

The capacity to implement these solutions will vary greatly by market, based on local climate, energy mix, and other considerations such as worker safety.

AI Plugins: The Future Generation of Ecosystems

Numerous companies have offered third-party service plugins, allowing developers to connect additional data sources into their language model, possibly reshaping data center ecosystems around certain sectors or data sources.

Conclusion

Since demand for data storage is anticipated to surpass supply, the data centre sector must adopt flexible methods to manage the AI revolution and add capacity in the right markets.

[ailtrahq]

Tether, a stablecoin issuer, has acquired an undisclosed stake in German-based crypto miner Northern Data Group. This strategic investment hints at potential collaborations in artificial intelligence (AI). Tether made this investment through its group company, Damoon. However, Tether has not disclosed the amount of capital allocated for this partnership. Ardoino Describes Tether’s Dive into Tech Industry Although a speculative report by Forbes reported a 420-million transaction, Tether neither confirmed nor refuted the exact figure. Moreover, prior discussions between Tether and Northern Data in July revealed that the stablecoin giant had ambitions to bolster Damoon, a Tether group company, before it finalized the acquisition. By doing so, Damoon aimed to procure the latest GPU hardware. Reflecting on the venture, Paolo Ardoino, Tether’s CTO, portrayed it as a dive into the new technological industry. Additionally, the company stated that this investment wouldn’t dip into its reserves, ensuring customer funds remain unaffected. Tether’s past has been controversial, especially in the U.S., where it faced legal challenges over its reserve transparency, leading to hefty fines and increased scrutiny. Tether Expands Global Reach with Diverse Partnerships Beyond its recent stake in Northern Data, Tether’s global influence is evident with Partnerships, from collaborating with KriptonMarket in Argentina to signing an MOU to bolster peer-to-peer (P2P) infrastructure in Georgia. Additionally, Ardoino hinted at some of the firm’s mining activities taking root in Latin America.  Coingape has recently reported that Tether Holdings has initiated USDT stablecoin loans to clientele. This move, however, has raised eyebrows. It’s been a year since Tether pledged to abstain from proffering secured loans. The company’s dominance in the stablecoin world makes this development noteworthy.  Source

[digital-trends-unleased]

The best Black Friday gaming PC deals for 2022

Missed out on finding the ideal bargain amidst the black Friday deals and sales yesterday? No need to fret, as there are still remarkable Black Friday deals available for gaming PCs. Should you be in the market for a fresh gaming PC, we've curated a selection of the finest deals currently accessible. Whether you're working with a modest budget or aiming to procure a top-tier gaming rig, there's an option catered to you in this compilation. Take a glance at the ongoing offers highlighted below — it's important to note, however, that these deals might not linger. Thus, if something catches your eye, seize the opportunity apple black Friday deals without delay! CyberPowerPC Gamer Xtreme — $850, was $1,100 Let's delve into one of the initial Black Friday gaming PC offers, which showcases an RTX 3050 – a graphics card not commonly found in desktops, given its usual association with laptops. However, it boasts additional commendable attributes such as a 12th-gen Intel Core 5-12600KF, a robust mid-tier CPU, along with 16GB of RAM. Collectively, these components contribute to a relatively seamless operating system experience. The package also entails 500GB of storage, albeit on the lower end, prompting consideration for an additional HDD or SSD to complement it. If you're a novice seeking to avoid the complexities of building a PC from scratch, the Gamer Xtreme presents an exceptional choice, especially since CyberPowerPC sweetens the deal with a quality mouse and keyboard inclusion. Legion Tower 5i Gen 6 — $1,100, was $1,540 We hold the Legion Tower 5i in such high regard that we've granted it a place within our compilation of the finest gaming desktop PCs. Beneath its exterior, it houses an RTX 3060 – a significantly more potent graphics card in comparison to an RTX 3050. Opting for at least an RTX 3060 is advisable when transitioning to a desktop, making it a worthwhile upgrade if within your means. The presence of an 11th-gen Intel i5-11400 further enhances its performance, offering a robust mid-range CPU capable of smoothly handling CPU-intensive games such as simulations and strategy titles. The configuration of 16GB RAM in a dual-channel setup bolsters performance, while the inclusion of a 650W PSU leaves room for future expansions. Conversely, the 500GB SSD capacity, although functional, falls on the lower side for a gaming desktop. To address this, considering one of the ongoing external hard drive deals could be prudent. Lenovo sweetens the deals best black friday by bundling in three months of Xbox Game Pass, providing an appealing extra perk. Alienware Aurora R13 — $1,300, was $1,872 Elevating the GPU a level higher, here's a best black Friday deals gaming PC deal that not only catches the eye but is also a testament to Alienware's knack for aesthetics. Sporting an RTX 3060 Ti, this offer promises a performance boost, translating to those coveted extra frames. Accompanied by the 12th-gen Intel i7-12700F, the system boasts ample power for streaming on platforms like Twitch or YouTube. Moreover, this enhanced CPU prowess introduces versatility, enabling tasks such as music and video editing to complement your gaming experience. Marking a notable addition, this entry boasts 16GB of DDR5 RAM, the fastest variant available in the market – an unexpected delight within this price bracket. Regrettably, storage stands at a modest 500GB SSD, likely necessitating a future upgrade due to the expanding size of modern games. Legion Tower 7i — $1,630, was $2,240 The Legion Tower 7i emerges as a noteworthy contender among the Black Friday gaming PC deals, presenting a harmonious fusion of performance and affordability, particularly evident with its inclusion of an RTX 3070. This equips it to effortlessly handle 2K at 144Hz – a resolution and refresh rate commonly found in the gaming monitor deals available. With the applied discount, the possibility of securing an outstanding monitor to complement the setup without surpassing a $2,000 budget seems quite feasible. Even though the 11th-gen Intel i7-11700K isn't the latest iteration, its prowess remains substantial for gaming, editing, and streaming tasks. Regrettably, DDR5 RAM isn't part of the package, yet the dual-channel configuration of the 16GB DDR4 RAM contributes to a marginal performance enhancement. The 650W power supply serves as a promising foundation for potential upgrades, although they might not be necessary given the generous storage setup – a 1TB HDD and 500GB SSD combo – providing ample space to last a year or more, if not longer. Alienware R14 Aurora Ryzen Edition — $2,100, was $2,780 Presenting our final offer, an absolute powerhouse housing an RTX 3080 Ti beneath its exterior, enabling seamless 4K gaming at comfortably smooth frame rates. While surpassing the 100 mark might be a challenge, particularly on the latest titles, the visual splendor at this level can easily overshadow such concerns. The presence of the AMD Ryzen 7 5800X CPU further contributes to its might, a processor that, in typical AMD fashion, excels in multi-threading tasks such as audio editing and streaming. Once more, the absence of DDR5 RAM is notable; however, the provided 16GB of DDR4 proves to be more than adequate, leaving room for future upgrades or replacements. The substantial 1TB SSD enhances the deal, offering ample storage that should remain sufficient, even considering the growing size of contemporary games. On the whole, the R14 emerges as a prime contender for one of the finest Black Friday deals for a premium gaming desktop, particularly one boasting an RTX 3080 Ti.       Read the full article

[lanshengic]

UK in talks with Nvidia, AMD, Intel and more to buy AI chips

【Lansheng Technology Information】According to the British Daily Telegraph, in order to catch up in the global computing power race, British Prime Minister Sunak will spend up to 100 million pounds of taxpayer funds to buy thousands of high-performance artificial intelligence chips. Government officials have been in discussions with IT giants Nvidia, AMD and Intel to procure equipment for a national "artificial intelligence research resource" as part of Sunak's ambition to make the UK a global leader in the field.

The effort, led by the science funding agency UK Research and Innovation, is believed to be in the advanced stages of an order for 5,000 GPUs from Nvidia, whose chips power AI models such as ChatGPT.

UK Chancellor of the Exchequer Jeremy Hunt set aside £900m for computing resources in March, although much of it is expected to go to traditional "Exascale" supercomputers. AI resources are believed to be allocated just over £50m, but are expected to rise to £70m-£100m as the world scrambles for AI chips.

Last week, the Financial Times reported that Saudi Arabia had bought at least 3,000 Nvidia H100 GPUs, and tech giants like Microsoft, Amazon, and Google were also racing to secure tens of thousands of much-needed chips while Biden, under national security powers, blocked Nvidia sells the chips in China.

Lansheng Technology Limited, which is a spot stock distributor of many well-known brands, we have price advantage of the first-hand spot channel, and have technical supports. 

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[shahananasrin-blog]

[ad_1] In an unmarked office building in Austin, Texas, two small rooms contain a handful of Amazon employees designing two types of microchips for training and accelerating generative AI. These custom chips, Inferentia and Trainium, offer AWS customers an alternative to training their large language models on Nvidia GPUs, which have been getting difficult and expensive to procure. "The entire world would like more chips for doing generative AI, whether that's GPUs or whether that's Amazon's own chips that we're designing," Amazon Web Services CEO Adam Selipsky told CNBC in an interview in June. "I think that we're in a better position than anybody else on Earth to supply the capacity that our customers collectively are going to want."Yet others have acted faster, and invested more, to capture business from the generative AI boom. When OpenAI launched ChatGPT in November, Microsoft gained widespread attention for hosting the viral chatbot, and investing a reported $13 billion in OpenAI. It was quick to add the generative AI models to its own products, incorporating them into Bing in February. That same month, Google launched its own large language model, Bard, followed by a $300 million investment in OpenAI rival Anthropic. It wasn't until April that Amazon announced its own family of large language models, called Titan, along with a service called Bedrock to help developers enhance software using generative AI."Amazon is not used to chasing markets. Amazon is used to creating markets. And I think for the first time in a long time, they are finding themselves on the back foot and they are working to play catch up," said Chirag Dekate, VP analyst at Gartner.Meta also recently released its own LLM, Llama 2. The open-source ChatGPT rival is now available for people to test on Microsoft's Azure public cloud.Chips as 'true differentiation'In the long run, Dekate said, Amazon's custom silicon could give it an edge in generative AI. "I think the true differentiation is the technical capabilities that they're bringing to bear," he said. "Because guess what? Microsoft does not have Trainium or Inferentia," he said.AWS quietly started production of custom silicon back in 2013 with a piece of specialized hardware called Nitro. It's now the highest-volume AWS chip. Amazon told CNBC there is at least one in every AWS server, with a total of more than 20 million in use. AWS started production of custom silicon back in 2013 with this piece of specialized hardware called Nitro. Amazon told CNBC in August that Nitro is now the highest volume AWS chip, with at least one in every AWS server and a total of more than 20 million in use.Courtesy AmazonIn 2015, Amazon bought Israeli chip startup Annapurna Labs. Then in 2018, Amazon launched its Arm-based server chip, Graviton, a rival to x86 CPUs from giants like AMD and Intel."Probably high single-digit to maybe 10% of total server sales are Arm, and a good chunk of those are going to be Amazon. So on the CPU side, they've done quite well," said Stacy Rasgon, senior analyst at Bernstein Research.Also in 2018, Amazon launched its AI-focused chips. That came two years after Google announced its first Tensor Processor Unit, or TPU. Microsoft has yet to announce the Athena AI chip it's been working on, reportedly in partnership with AMD. CNBC got a behind-the-scenes tour of Amazon's chip lab in Austin, Texas, where Trainium and Inferentia are developed and tested. VP of product Matt Wood explained what both chips are for."Machine learning breaks down into these two different stages. So you train the machine learning models and then you run inference against those trained models," Wood said. "Trainium provides about 50% improvement in terms of price performance relative to any other way of training machine learning models on AWS."Trainium first came on the market in 2021, following the 2019 release of Inferentia, which is now on its second generation.Trainum allows customers "to deliver very, very low-cost, high-throughput, low-latency, machine learning inference, which is all the predictions of when you type in a prompt into your generative AI model, that's where all that gets processed to give you the response, " Wood said.For now, however, Nvidia's GPUs are still king when it comes to training models. In July, AWS launched new AI acceleration hardware powered by Nvidia H100s. "Nvidia chips have a massive software ecosystem that's been built up around them over the last like 15 years that nobody else has," Rasgon said. "The big winner from AI right now is Nvidia."Amazon's custom chips, from left to right, Inferentia, Trainium and Graviton are shown at Amazon's Seattle headquarters on July 13, 2023.Joseph HuertaLeveraging cloud dominanceAWS' cloud dominance, however, is a big differentiator for Amazon."Amazon does not need to win headlines. Amazon already has a really strong cloud install base. All they need to do is to figure out how to enable their existing customers to expand into value creation motions using generative AI," Dekate said.When choosing between Amazon, Google, and Microsoft for generative AI, there are millions of AWS customers who may be drawn to Amazon because they're already familiar with it, running other applications and storing their data there."It's a question of velocity. How quickly can these companies move to develop these generative AI applications is driven by starting first on the data they have in AWS and using compute and machine learning tools that we provide," explained Mai-Lan Tomsen Bukovec, VP of technology at AWS.AWS is the world's biggest cloud computing provider, with 40% of the market share in 2022, according to technology industry researcher Gartner. Although operating income has been down year-over-year for three quarters in a row, AWS still accounted for 70% of Amazon's overall $7.7 billion operating profit in the second quarter. AWS' operating margins have historically been far wider than those at Google Cloud.AWS also has a growing portfolio of developer tools focused on generative AI."Let's rewind the clock even before ChatGPT. It's not like after that happened, suddenly we hurried and came up with a plan because you can't engineer a chip in that quick a time, let alone you can't build a Bedrock service in a matter of 2 to 3 months," said Swami Sivasubramanian, AWS' VP of database, analytics and machine learning.Bedrock gives AWS customers access to large language models made by Anthropic, Stability AI, AI21 Labs and Amazon's own Titan."We don't believe that one model is going to rule the world, and we want our customers to have the state-of-the-art models from multiple providers because they are going to pick the right tool for the right job," Sivasubramanian said.An Amazon employee works on custom AI chips, in a jacket branded with AWS' chip Inferentia, at the AWS chip lab in Austin, Texas, on July 25, 2023.Katie TarasovOne of Amazon's newest AI offerings is AWS HealthScribe, a service unveiled in July to help doctors draft patient visit summaries using generative AI. Amazon also has SageMaker, a machine learning hub that offers algorithms, models and more. Another big tool is coding companion CodeWhisperer, which Amazon said has enabled developers to complete tasks 57% faster on average. Last year, Microsoft also reported productivity boosts from its coding companion, GitHub Copilot. In June, AWS announced a $100 million generative AI innovation "center." "We have so many customers who are saying, 'I want to do generative AI,' but they don't necessarily know what that means for them in the context of their own businesses. And so we're going to bring in solutions architects and engineers and strategists and data scientists to work with them one on one," AWS CEO Selipsky said.Although so far AWS has focused largely on tools instead of building a competitor to ChatGPT, a recently leaked internal email shows Amazon CEO Andy Jassy is directly overseeing a new central team building out expansive large language models, too.In the second-quarter earnings call, Jassy said a "very significant amount" of AWS business is now driven by AI and more than 20 machine learning services it offers. Some examples of customers include Philips, 3M, Old Mutual and HSBC. The explosive growth in AI has come with a flurry of security concerns from companies worried that employees are putting proprietary information into the training data used by public large language models."I can't tell you how many Fortune 500 companies I've talked to who have banned ChatGPT. So with our approach to generative AI and our Bedrock service, anything you do, any model you use through Bedrock will be in your own isolated virtual private cloud environment. It'll be encrypted, it'll have the same AWS access controls," Selipsky said.For now, Amazon is only accelerating its push into generative AI, telling CNBC that "over 100,000" customers are using machine learning on AWS today. Although that's a small percentage of AWS's millions of customers, analysts say that could change."What we are not seeing is enterprises saying, 'Oh, wait a minute, Microsoft is so ahead in generative AI, let's just go out and let's switch our infrastructure strategies, migrate everything to Microsoft.' Dekate said. "If you're already an Amazon customer, chances are you're likely going to explore Amazon ecosystems quite extensively."— CNBC's Jordan Novet contributed to this report. [ad_2]

[jcmarchi]

Do Economic Bans Work? Nvidia Chips Still End Up in China - Technology Org

New Post has been published on https://thedigitalinsider.com/do-economic-bans-work-nvidia-chips-still-end-up-in-china-technology-org/

Do Economic Bans Work? Nvidia Chips Still End Up in China - Technology Org

Chinese military organizations, state-affiliated artificial intelligence research institutions, and universities have procured relatively small quantities of Nvidia semiconductors, despite the U.S. ban on their export to China.

H100 platform. Image credit: NVIDIA

According to a Reuters analysis, these purchases were conducted last year and were facilitated by relatively unknown Chinese suppliers. Such facts underscore the challenges faced by Washington in preventing China’s access to advanced U.S. chips, particularly those with applications in artificial intelligence and high-performance computing for military use.

Despite export restrictions, the publicly available tender documents reveal numerous instances of Chinese entities acquiring Nvidia semiconductors, including the A100, H100, A800, and H800 chips, after the bans were implemented.

Nvidia’s graphic processing units (GPUs) are widely acknowledged for their superior performance in AI tasks, efficiently handling large datasets required for machine learning. The persistent demand for and access to banned Nvidia chips is driven by the limited alternatives available to Chinese firms, despite emerging competition from companies like Huawei.

Nvidia previously dominated China’s AI chip market with a 90% share before the imposition of export restrictions.

Buyers include prestigious universities and entities, such as the Harbin Institute of Technology and the University of Electronic Science and Technology of China, both subject to U.S. export restrictions. Local vendors reportedly acquire excess stock from large U.S. firms or import through companies in regions like India, Taiwan, and Singapore.

The Reuters analysis covers over 100 tenders where state entities acquired A100 chips, and post-October bans reveal A800 purchases in dozens of tenders. Documents indicate Tsinghua University procured two H100 chips, and a lab under the Ministry of Industry and Information Technology obtained one. A military entity in Wuxi sought three A100 chips in October and one H100 chip in the current month.

Although military tenders are typically redacted, most indicate AI usage. While the quantities are small, they can perform complex machine-learning tasks and enhance existing AI models.

Written by Alius Noreika

[ricardojordao9]

Critérios para Escolher o Melhor Notebook Gamer

Escolher um notebook gamer pode ser uma tarefa complicada, especialmente com tantas opções disponíveis no mercado. Você deve considerar vários fatores antes de tomar sua decisão, desde o desempenho gráfico até a qualidade da construção. Neste artigo, vamos abordar as principais dicas para escolher o notebook gamer perfeito para suas necessidades. Se você está na dúvida entre Asus ou Lenovo, por exemplo, este guia vai ajudá-lo a esclarecer suas dúvidas.

Entenda suas Necessidades

O primeiro passo para escolher o notebook gamer ideal é entender suas próprias necessidades. Você pretende jogar jogos mais leves ou títulos AAA que demandam alto desempenho? Sua escolha deve refletir os tipos de jogos que você deseja jogar. Além disso, considere se você também usará o notebook para outras tarefas, como trabalho ou estudo. Em casos como esses, um notebook ideal para trabalho pode ser uma opção versátil.

Desempenho Gráfico

O desempenho gráfico é um dos aspectos mais críticos em um notebook gamer. Placas gráficas dedicadas são essenciais para uma experiência de jogo fluida e visualmente impressionante. Marcas como NVIDIA e AMD são líderes no mercado de GPUs. Certifique-se de escolher um modelo que atenda às especificações dos jogos que você pretende jogar. Jogos mais exigentes requerem GPUs mais potentes, como as séries RTX da NVIDIA.

Processador e Memória RAM

O processador (CPU) e a memória RAM são fundamentais para o desempenho geral do notebook. Processadores Intel Core i7 ou i9, e AMD Ryzen 7 ou 9, são altamente recomendados para jogos intensivos. Em termos de memória RAM, 16GB é o mínimo sugerido para um desempenho eficiente, embora 32GB ofereça uma margem maior para multitarefa e jogos futuros.

Tela e Resolução

A qualidade da tela também é crucial. Procure por notebooks com resoluções de pelo menos Full HD (1920x1080). Para uma experiência mais imersiva, considere telas com taxa de atualização alta (144Hz ou mais). Essas características não só melhoram a qualidade visual dos jogos, mas também proporcionam uma vantagem competitiva em jogos rápidos.

Armazenamento

Armazenamento é outro fator a considerar. Notebooks com SSDs (Solid State Drives) são preferíveis devido à sua velocidade superior em comparação aos HDDs (Hard Disk Drives). Um SSD de 512GB ou mais é ideal para garantir espaço suficiente para seus jogos e outros arquivos. Alguns modelos oferecem combinações de SSD e HDD, proporcionando o melhor dos dois mundos.

Qualidade de Construção e Resfriamento

A construção do notebook gamer deve ser robusta para suportar o uso intenso. Além disso, sistemas de resfriamento eficientes são essenciais para manter a performance ideal e evitar superaquecimento. Modelos com ventilação adequada e tecnologia de resfriamento avançada, como heat pipes e ventoinhas adicionais, são altamente recomendados.

Bateria e Portabilidade

Embora os notebooks gamers não sejam famosos pela longa duração da bateria, é importante considerar a autonomia para uso em movimento. Modelos mais leves e portáteis são ideais se você planeja transportar seu notebook com frequência. Lembre-se, porém, que a portabilidade pode vir à custa do desempenho.

Opções de Conectividade

A conectividade é um aspecto frequentemente negligenciado. Certifique-se de que o notebook possui portas suficientes para todos os seus periféricos, como teclado, mouse, e headset. Portas USB, HDMI, e Ethernet são essenciais. A conectividade sem fio, como Wi-Fi 6 e Bluetooth 5.0, também são bons diferenciais.

Software e Garantia

Verifique se o notebook vem com software útil, como utilitários de gestão de desempenho e atualizações de driver. Além disso, uma garantia abrangente pode oferecer tranquilidade adicional, cobrindo possíveis defeitos de fabricação ou falhas de hardware.

Pesquise e Compare

Antes de finalizar sua compra, é crucial pesquisar e comparar diferentes modelos. Utilize sites de reviews, fóruns e vídeos de unboxing para obter uma visão clara sobre o desempenho real dos notebooks. Escolher o melhor notebook para estudantes ou o notebook perfeito para jogos pode depender das análises e experiências de outros usuários.

Escolher o notebook gamer ideal envolve equilibrar vários fatores, desde desempenho e qualidade da tela até armazenamento e conectividade. A decisão final deve refletir suas necessidades específicas e o orçamento disponível. Não se apresse, faça sua pesquisa, e considere todas as opções antes de tomar a decisão final.

[grobaltech]

Complete review of 15" MacBook Air 👇

The 15-inch MacBook Air is a powerful and versatile laptop that offers a great balance of performance, portability, and battery life. It is powered by the new M2 chip, which delivers up to 18% faster CPU performance and up to 35% faster GPU performance than the previous generation. The 15-inch Retina display is also a major upgrade, with a wider color gamut and higher brightness.

The MacBook Air is still incredibly thin and light, weighing just 3.3 pounds and measuring just 0.63 inches thick. It also has an excellent battery life, lasting up to 15 hours on a single charge.

Overall, the 15-inch MacBook Air is a great choice for anyone who needs a powerful and portable laptop. It is perfect for students, professionals, and creatives alike.

Here are some of the pros and cons of the 15-inch MacBook Air:

😱😱Pros:😱😱

😱Powerful M2 chip

😱Beautiful 15-inch Retina display

😱Long battery life

😱Thin and light design

😱Excellent performance for everyday tasks and creative work

😭😭Cons:😭😭

😭No fan, so it can get warm under heavy load

😭No Touch Bar

😭No SD card slot

😭Expensive

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Overall, the 15-inch MacBook Air is a great choice for anyone who needs a powerful and portable laptop. It is perfect for students, professionals, and creatives alike. However, it is important to note that it is a bit expensive, and it does not have some features that some users may find important, such as a fan, Touch Bar, or SD card slot.

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[unicornplatform]

The Ultimate Guide to Pricing Your AI App Development

Are you curious about the costs involved in developing an AI app?

AI technology holds immense potential for businesses across various sectors, offering valuable insights and predictive analysis. From healthcare applications to customer support services, AI is progressively becoming a vital tool for many companies. However, the financial aspect of developing an AI application can be substantial and varies significantly depending on multiple factors.

In this guide, we will explore the different costs associated with AI app development and examine the numerous aspects that could influence the total price. Additionally, we'll offer some advice on minimizing expenses while creating your own app so that you can maximize your budget when building a powerful and impressive AI application.

Understanding Artificial Intelligence (AI) Apps

As we progress into an era where Artificial Intelligence (AI) has become integrated into our daily lives – from digital assistants on smartphones to voice-activated tools at home – demand for AI-driven apps continues to grow. These apps serve various purposes but what exactly do they accomplish?

AI apps are designed to enhance user experiences, automate tasks and provide data-driven insights into consumer behavior patterns across industries such as retail or manufacturing. Capable of handling vast amounts of information using sophisticated algorithms, these apps can detect patterns otherwise unattainable by human observation alone; giving users access to invaluable intel like customer service improvement areas or predicting future purchases based on past trends.

Developing an AI app involves combining programming languages with algorithms alongside libraries and APIs specific to each project's unique requirements; which ultimately determines its cost along with factors including data storage capacity needs or platform preferences among others.

Factors Influencing The Cost Of Developing An_AI_App

The overall expense incurred during the development process depends largely upon several key elements:

Expertise Level: Skilled developers well-versed in industry standards are essential for delivering projects within stipulated budgets.

Platform Choice: Platform-specific constraints may affect development costs. However, AI apps are typically created for multiple platforms to maximize user reach and engagement.

Feature Inclusions: Integrating additional features or complex functionalities can increase required expertise and resources, necessitating accurate consideration during cost estimation.

Each project comes with its unique set of requirements; thus careful planning at every stage is crucial to ensure precise budgeting.

Determining the Price of Your AI App

To establish an appropriate price for your AI app, consider the following factors:

Design & Development Expenses

Budget allocation should account for aspects like data cleaning, database setup, API integration, algorithm design as well as coding processes. Depending on project size and scope - these costs could range from a few hundred dollars up to tens of thousands or more.

Cloud Computing Costs

Incorporating cloud services like Amazon Web Services (AWS) or Google Cloud Platform incurs additional expenses such as storage capacity fees while using GPUs to run Machine Learning models increases overall operational expenditures even further.

Maintenance Costs

Post-release maintenance – including regular updates/bug fixes; staff training; hardware upgrades and customer support services – will continue adding ongoing expenses if not properly accounted for beforehand!

Supplementary Resources For Developing An_AI_App

Developing an AI app often requires extra resources such as datasets, hardware infrastructure along with specialized software developers whose fees depend primarily on their skill level/experience per hour rates:

Datasets

Procuring extensive datasets or generating self-sourced data can be costly but necessary in many cases. Professional data labeling also adds considerable expense depending upon dataset type/quality needed ($50-$10k+).

Hardware Infrastructure

Investments into computational power/storage capacities contribute significantly towards monthly resource allocations ranging anywhere between $100-$15k.

Software Development

Depending upon location/availability concerns: hourly rates charged by experienced developers vary between $50-$250, with app development itself requiring hundreds of hours' worth labor.

Creating a Landing Page for Your AI App

Developing an effective landing page for your AI app can be costly; however, no-code platforms like Unicorn Platform offer quick and cost-effective alternatives. With its drag & drop interface and AI technology integration, building an engaging landing page is now easier than ever before - even without technical expertise!

Conclusion

In conclusion, the cost of developing an AI app depends on various factors such as project complexity/functionality along with chosen developers' experience levels. Careful evaluation of elements like market research/analysis or hardware/backend development plays a crucial role in determining overall expenses.

Opting for no-code platforms like Unicorn Platform streamlines the process while remaining budget-friendly – making it the perfect solution for startups/mobile apps/SaaS businesses looking to create powerful yet affordable AI applications!

Inspired by: Unicorn Platform's blog post

Made by AI.