#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.

[jcmarchi]

The AI Explosion Continues in 2025: What Organizations Should Anticipate This Year

New Post has been published on https://thedigitalinsider.com/the-ai-explosion-continues-in-2025-what-organizations-should-anticipate-this-year/

The AI Explosion Continues in 2025: What Organizations Should Anticipate This Year

With AI forecasted to continue its explosion in 2025, the ever-evolving technology presents both unprecedented opportunities and complex challenges for organizations worldwide. To help today’s organizations and professionals secure the most value from AI in 2025, I’ve shared my thoughts and anticipated AI trends for this year.

Organizations Must Strategically Plan for the Cost of AI

The world continues to be ecstatic about the potential of AI. However, the cost of AI innovation is a metric that organizations must plan for. For example, AI needs GPUs, however many CSPs have larger deployments of N-1, N-2 or older GPUs which weren’t built exclusively for AI workloads. Also, cloud GPUs can be cost prohibitive at scale and easily switched on for developers as projects grow/scale (more expense); additionally, buying GPUs (if able to procure due to scarcity) for on-prem use can also be a very expensive proposition with individual chips costing well into the tens of thousands of dollars. As a result, server systems built for demanding AI workloads are becoming cost prohibitive or out of reach for many with capped departmental operating expenses (OpEx) budgets. In 2025, enterprise customers must level-set their AI costs and re-sync levels of AI development budget. With so many siloed departments now taking initiative and building their own AI tools, companies can inadvertently be spending thousands per month on small or siloed uses of cloud-based GPU and their requirement for AI compute instances, which all mount up (especially if users leave these instances running).

Open-Source Models Will Promote the Democratization of Several AI Use Cases

In 2025, there will be immense pressure for organizations to prove ROI from AI projects and associated budgets. With the cost leveraging low code or no code tools provided by popular ISVs to build AI apps, companies will continue to seek open-source models which are more easily fine tuned rather than training and building from scratch. Fine-tuning open-source models more efficiently use available AI resources (people, budget and/or compute power), helping explain why there are currently over 900K+ (and growing) models available for download at Hugging Face alone. However, when enterprises embark on open-source models, it will be critical to secure and police the use of open-source software, framework, libraries and tools throughout their organizations. Lenovo’s recent agreement with Anaconda is a great example of this support, where the Intel-powered Lenovo Workstation portfolio and Anaconda Navigator help streamline data science workflows.

AI Compliance Becomes Standard Practice

Shifts in AI policy will see the computing of AI move closer to the source of company data, and more on-premises (especially for the AI Development phases of a project or workflow). As AI becomes closer to the core of many businesses, it will move from a separate parallel or special workstream to that in line with many core business functions. Making sure AI is compliant and responsible is a real objective today, so as we head into 2025 it will become more of a standard practice and form part of the fundamental building blocks for AI projects in the enterprise. At Lenovo, we have a Responsible AI Committee, comprised of a diverse group of employees who ensure solutions and products meet security, ethical, privacy, and transparency standards. This group reviews AI usage and implementation based on risk, applying security policies consistently to align with a risk stance and regulatory compliance. The committee’s inclusive approach addresses all AI dimensions, ensuring comprehensive compliance and overall risk reduction.

Workstations Emerge as Efficient AI Tools In and Out of the Office

Using workstations as more powerful edge and departmental based AI appliances is already on the increase. For example, Lenovo’s Workstation portfolio, powered by AMD, helps media and entertainment professionals bridge the gap between expectations and the resources needed to deliver the highest-fidelity visual content. Thanks to their small form factor and footprint, low acoustic, standard power requirements, and use of client-based operating systems, they can be easily deployed as AI inference solutions where more traditional servers may not fit. Another use case is within standard industry workflows where AI enhanced data analytics can deliver real business value and is VERY line of sight to C suite execs trying to make a difference. Other use cases are the smaller domain specific AI tools being created by individuals for their own use. These efficiency savings tools can become AI superpowers and can include everything from MS Copilot, Private Chatbots to Personal AI Assistants.

Maximize AI’s Potential in 2025

AI is one of the fastest-growing technological evolutions of our era, breaking into every industry as a transformative technology that will enhance efficiency for all – enabling faster and more valuable business outcomes.

AI, including machine and deep learning and generative AI with LLMs, requires immense compute power to build and maintain the intelligence needed for seamless customer AI experiences. As a result, organizations should ensure they leverage high-performing and secure desktop and mobile computing solutions to revolutionize and enhance the workflows of AI professionals and data scientists.

[andronetalks]

Did DeepSeek Use Shell Companies In Singapore To Procure Nvidia Blacklisted Chips?

Zero Hedge by Tyler DurdenFriday, Jan 31, 2025 – 07:20 AM Chinese startup DeepSeek’s latest AI models, V3 and R1, were reportedly trained on just 2,048 Nvidia H800 chips—GPUs that were blacklisted by the US government in 2023 for sale to Chinese firms. Federal investigators are now combing through trade data to determine how DeepSeek acquired the restricted chips. Read more…

[the-latest-research]

AI in Supply Chain Market to be Worth $58.55 Billion by 2031

Meticulous Research®—a leading global market research company, published a research report titled, ‘AI in Supply Chain Market by Offering (Hardware, Software, Other), Technology (ML, NLP, RPA, Other), Deployment Mode, Application (Demand Forecasting, Other), End-use Industry (Manufacturing, Retail, F&B, Other) & Geography - Global Forecast to 2031’

According to this latest publication from Meticulous Research®, the AI in supply chain market is projected to reach $58.55 billion by 2031, at a CAGR of 40.4% from 2024 to 2031. The growth of the AI in supply chain market is driven by the increasing incorporation of artificial intelligence in supply chain operations and the rising need for greater visibility & transparency in supply chain processes. However, the high procurement & operating costs of AI-based supply chain solutions and the lack of supporting infrastructure restrain the growth of this market.

Furthermore, the growing demand for AI-based business automation solutions is expected to generate growth opportunities for the players operating in this market. However, performance issues in integrating data from multiple sources and data security & privacy concerns are major challenges impacting market growth. Additionally, the rising demand for cloud-based supply chain solutions is a prominent trend in the AI in supply chain market.

Based on offering, the AI in supply chain market is segmented into hardware, software, and services. In 2024, the hardware segment is expected to account for the largest share of 44.4% of the AI in supply chain market. The large market share of this segment is attributed to advancements in data center capabilities, the growing need for storage hardware due to increasing storage requirements for AI applications, the crucial need for constant connectivity in the supply chain operations, and the emphasis on product development and enhancement by manufacturers. For instance, in January 2023, Intel Corporation launched its 4th Gen Intel Xeon Scalable processors (code-named Sapphire Rapids), the Intel Xeon CPU Max Series (code-named Sapphire Rapids HBM), and the Intel Data Center GPU Max Series (code-named Ponte Vecchio). These new processors deliver significant improvements in data center performance, efficiency, security, and AI capabilities.

However, the software segment is expected to record the highest CAGR of 43.2% during the forecast period. This segment's growth is driven by the rising focus on product development and the enhancement of supply chain software and the benefits offered by supply chain software in facilitating supply chain visibility and centralized operations.

Download Sample Report Here @ https://www.meticulousresearch.com/download-sample-report/cp_id=5064

Based on technology, the AI in supply chain market is segmented into machine learning, computer vision, natural language processing, context-aware computing, and robotic process automation. In 2024, the machine learning segment is expected to account for the largest share of 63.0% of the AI in supply chain market. The large market share of this segment is attributed to the advancements in data center capabilities, increasing deployment of machine learning solutions and its ability to perform tasks without relying on human input, and the rapid adoption of cloud-based technology across several industries. For instance, in June 2022, FedEx Corporation (U.S.) invested in FourKites, Inc. (U.S.), a supply chain visibility startup. This strategic collaboration allows FedEx to leverage its machine learning and AI capabilities with data from FedEx, enhancing its operational efficiency and visibility.

However, the robotic process automation segment is expected to record the highest CAGR of 42.9% during the forecast period. The growth of this segment is driven by the increased adoption of RPA across various industries and the rising demand for automating business processes to meet heightened customer expectations.

Based on deployment mode, the AI in supply chain market is segmented into cloud-based deployments and on-premise deployments. In 2024, the cloud-based deployments segment is expected to account for the larger share of 75.6% of the AI in supply chain market. The large market share of this segment is attributed to the increasing avenues for cloud-based deployments, the superior flexibility and affordability offered by cloud-based deployments, and the increasing adoption of cloud-based solutions by small & medium-sized enterprises.

Moreover, the cloud-based deployments segment is expected to record the highest CAGR during the forecast period. The rapid development of new security measures for cloud-based deployments is expected to drive this segment's growth in the coming years.

Based on application, the AI in supply chain market is segmented into demand forecasting, supply chain planning, warehouse management, fleet management, risk management, inventory management, predictive maintenance, real-time supply chain visibility, and other applications. In 2024, the demand forecasting segment is expected to account for the largest share of 25.2% of the AI in supply chain market. The large market share of this segment is attributed to the rising initiatives to integrate AI capabilities in supply chain solutions, dynamic changes in customer behaviors and expectations, and the rising need to achieve accuracy and resilience in the supply chain. For instance, in March 2023, Zionex, Inc. (South Korea), a prominent provider of advanced supply chain and integrated business planning platforms, launched PlanNEL Beta. This AI-powered SaaS platform is designed for demand forecasting and inventory optimization.

However, the real-time supply chain visibility segment is expected to record the highest CAGR during the forecast period. This segment's growth is driven by the rising integration of AI capabilities into supply chains to obtain real-time data on them.

Based on end-use industry, the AI in supply chain market is segmented into manufacturing, food and beverage, healthcare & pharmaceuticals, automotive, retail, building & construction, medical devices & consumables, aerospace & defense, and other end-use industries. In 2024, the manufacturing segment is expected to account for the largest share of 23.1% of the AI in supply chain market. The large market share of this segment is attributed to the increasing number of manufacturing companies, favorable initiatives to integrate artificial capabilities in the supply chain, and the increasing focus on achieving accuracy and resilience in the supply chain among manufacturers.

However, the retail segment is expected to record the highest CAGR of 47.8% during the forecast period. This segment's growth is driven by the rising integration of AI capabilities in the retail supply chain to forecast inventory and demand and retailers' growing focus on meeting consumer expectations.

Based on geography, the AI in supply chain market is segmented into North America, Europe, Asia-Pacific, Latin America, and the Middle East & Africa. In 2024, Asia-Pacific is expected to account for the largest share of 36.9% of the AI in supply chain market. The large market share of this region is attributed to the rapid pace of digitalization and modernization across industries, the advent of Industry 4.0, and the growing adoption of advanced technologies across various businesses.

Moreover, the Asia-Pacific region is projected to record the highest CAGR of 42.7% during the forecast period. This market's growth is driven by the proliferation of advanced supply chain solutions, the rising deployment of AI tools across the region, and efforts by major market players to implement AI technology across various sectors.

Key Players

Some of the key players operating in the AI in supply chain market are IBM Corporation (U.S.), SAP SE (Germany), Microsoft Corporation (U.S.), Google LLC (U.S.), Amazon Web Services, Inc. (U.S.), Intel Corporation (U.S.), NVIDIA Corporation (U.S.), Oracle Corporation (U.S.), C3.ai, Inc. (U.S.), Samsung SDS CO., Ltd. (South Korea), Coupa Software Inc. (U.S.), Micron Technology, Inc. (U.S.), Advanced Micro Devices, Inc. (U.S.), FedEx Corporation (U.S.), and Deutsche Post DHL Group (Germany).

Complete Report Here : https://www.meticulousresearch.com/product/ai-in-supply-chain-market-5064

Key questions answered in the report:

Which are the high-growth market segments based on offering, technology, deployment mode, application, and end-use industry?

What was the historical market for AI in supply chain?

What are the market forecasts and estimates for the period 2024–2031?

What are the major drivers, restraints, and opportunities in the AI in supply chain market?

Who are the major players, and what shares do they hold in the AI in supply chain market?

What is the competitive landscape like in the AI in supply chain market?

What are the recent developments in AI in supply chain market?

What are the different strategies adopted by the major players in AI in supply chain market?

What are the key geographic trends, and which are the high-growth countries?

Who are the local emerging players in the AI in supply chain market, and how do they compete with the other players?

Contact Us: Meticulous Research® Email- sales@meticulousresearch.com Contact Sales- +1-646-781-8004 Connect with us on LinkedIn- https://www.linkedin.com/company/meticulous-research

[qsccloud9]

Reducing Time-to-Market with QSC Cloud GPUs

In today’s fast-paced technological landscape, enterprise-level companies and AI-driven startups are under immense pressure to stay ahead of the curve. With the rise of machine learning models, deep learning techniques, and big data analytics, the need for high-performance computing resources has never been greater. However, as these companies scale their AI projects, they often face significant challenges related to GPU availability, scalability, and cost efficiency. This is where QSC Cloud comes in, offering a powerful and flexible solution to accelerate AI workflows and reduce time-to-market.

The Challenges Facing AI-Driven Enterprises

For AI and machine learning teams, the ability to quickly develop, train, and deploy complex models is crucial. However, many organizations encounter common pain points in their journey:

Limited Access to High-Performance GPU Clusters: Advanced machine learning models require significant computational power. Traditional data centers may not have enough GPUs available or may involve lengthy approval processes to gain access to the necessary resources.

Scalability Issues: As AI projects grow, so do the computational demands. Many enterprises struggle to quickly scale their infrastructure to meet these rising demands, which can lead to delays in project timelines.

Long Setup Times and High Costs: Setting up and maintaining GPU infrastructure can be expensive and time-consuming. Enterprises often face high upfront costs when investing in their own hardware and the logistics involved in maintaining these systems.

These challenges result in slowed innovation and delayed time-to-market, which can directly impact a company’s competitive advantage in the AI space.

How QSC Cloud Can Help

QSC Cloud provides a cost-effective, flexible, and scalable solution to these challenges. By offering on-demand access to high-performance GPU clusters like the Nvidia H100 and H200, QSC Cloud empowers enterprises to seamlessly scale their AI workflows without the burden of managing complex hardware or worrying about infrastructure limitations.

Here’s how QSC Cloud helps reduce time-to-market:

1. On-Demand Access to High-Performance GPUs

With QSC Cloud, enterprises can instantly access powerful GPU clusters on-demand, whether they are running deep learning models, large-scale data analytics, or training AI systems. This eliminates the need for long procurement processes and gives teams the flexibility to rapidly access the compute power they need, when they need it.

2. Scalability to Meet Growing Demands

One of the biggest benefits of QSC Cloud is its scalability. As AI projects grow in complexity, so does the need for more computational power. QSC Cloud offers the flexibility to scale resources up or down as needed, without the hassle of investing in additional hardware or worrying about infrastructure limitations. This means teams can remain agile and responsive to project needs, reducing downtime and accelerating development cycles.

3. Cost Efficiency and Quick Setup

Traditional data centers often require significant upfront investment and maintenance costs. QSC Cloud, however, offers a pay-as-you-go model, allowing enterprises to only pay for the resources they actually use. This makes it easier to manage costs and prevents overspending on unused infrastructure. Additionally, with quick setup times and easy deployment, businesses can focus on their AI projects rather than worrying about managing hardware or infrastructure.

Accelerating Innovation with QSC Cloud

In the fast-moving world of AI development, time is of the essence. Delays in computational resources can lead to missed opportunities and delayed product releases. By using QSC Cloud, enterprises can streamline their workflows, reduce the time required for training and inferencing, and ultimately reduce their time-to-market. Whether you are a large enterprise or an AI-driven startup, QSC Cloud offers the scalability, cost-effectiveness, and speed needed to stay ahead of the competition and innovate faster.

With QSC Cloud, you can solve the challenges of GPU availability, scalability, and cost efficiency, enabling you to accelerate your AI projects and meet the growing demands of the market.

[canaldoshin]

Chrome mais leve usando menos memória RAM (Otimização)

Neste artigo vou mostrar como otimizar drasticamente o desempenho do Google Chrome. Nos testes em que eu realizei, percebi uma boa diferença no consumo de Memória RAM. Então, eu vou mostrar como fiz passo a passo para conseguir reduzir e deixar mais leve o Google Chrome. Com apenas 3 guias abertas esta consumindo mais de 1 GB de Memória. E essas 3 guias fazem com que fique aberto vários processos do Chrome em execução. Aprenda esta configuração avançada e depois você comenta se funcionou para você também beleza.

Deixando o Chrome mais leve e reduzindo o consumo de memória RAM

 1. Override Software Rendering List Primeiro abra o Chrome e digite no campo de endereços chrome://flags. Será exibido uma página de configuração avançada do Google Chrome. Nesta página você vai procurar por Override Software Rendering List. Ative e clique em Reiniciar para salvar as configurações.

  2. Smooth Scrolling Nesta próxima configuração, procure por Smooth Scrolling. (Ative esta configuração para uma animação mais suave nas rolagens de páginas da Web) e clique no botão Reiniciar.

3. GPU Rasterization Agora no campo de pesquisa desta mesma tela, digite GPU. Encontre a configuração GPU Rasterization e ative esta configuração e clique em Reiniciar.

Outras configurações - Clique nos 3 pontinhos laterais na parte superior e abra as configurações. - Na aba esquerda selecione Desempenho e desative 'Alertas de problemas de desempenho'. - Abaixo, em Economia de memória, estará provavelmente selecionada a opção Equilibrada, altere para Máximo - Em velocidade de páginas pré-carregadas selecione Pré-carregamento padrão. - Agora vamos na aba Aparência e vamos desativar a opção 'Mostrar imagens de prévia na guia'. - E por último, na aba Sistema desative as opções: 'Executar aplicativos em segundo plano quando o Google Chrome estiver fechado' e também 'Usar aceleração gráfica quando disponível'.

É Hora de ver o resultado Ao refazer o teste de consumo de Memória Ram no Chrome, observei que ao abrir 3 guias novamente, o consumo de memória foi reduzido de 1 GB para 650 megabytes, ganhando um resultado incrível e deixando o navegador mais leve. Se esta configuração que eu te passei der certo aí para você comenta aqui embaixo para eu saber que funcionou.

Read the full article

[govindhtech]

NayaOne Digital Sandbox Supports Financial Services Growth

Leaders in Fintech Use Generative AI to Provide Faster, Safer, and More Accurate Financial Services.

Ntropy, Contextual AI, NayaOne, and Securiti improve financial planning, fraud detection, and other AI applications with NVIDIA NIM microservices and quicker processing. A staggering 91% of businesses in the financial services sector (FSI) are either evaluating artificial intelligence or currently using it as a tool to improve client experiences, increase operational efficiency, and spur innovation.

Generative AI powered by NVIDIA NIM microservices and quicker processing may improve risk management, fraud detection, portfolio optimization, and customer service.

Companies like Ntropy, Contextual AI, and NayaOne all part of the NVIDIA Inception program for innovative startups are using these technologies to improve financial services applications.

Additionally, NVIDIA NIM is being used by Silicon Valley-based firm Securiti to develop an AI-powered copilot for financial services. Securiti is a centralized, intelligent platform for data and generative AI safety.

The businesses will show how their technology can transform heterogeneous, sometimes complicated FSI data into actionable insights and enhanced innovation possibilities for banks, fintechs, payment providers, and other organizations at Money20/20, a premier fintech conference taking place this week in Las Vegas.

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Ntropy Brings Order to Unstructured Financial Data

New York-based Ntropy Organizes Unstructured Financial Data Ntropy assists in clearing financial services processes of different entropy disorder, unpredictability, or uncertainty states.

By standardizing financial data from various sources and geographical locations, the company’s transaction enrichment application programming interface (API) serves as a common language that enables financial services applications to comprehend any transaction with human-like accuracy in milliseconds, at a 10,000x lower cost than conventional techniques.

The NVIDIA Triton Inference Server and Llama 3 NVIDIA NIM microservice use NVIDIA H100 Tensor Core GPUs. The Llama 3 NIM microservice increased Ntropy’s large language models (LLMs) usage and throughput by 20x compared to native models.

Using LLMs and the Ntropy data enricher, Airbase, a top supplier of procure-to-pay software platforms, improves transaction authorization procedures.

Ntropy will talk at Money20/20 about how their API may be used to clean up merchant data belonging to consumers, which increases fraud detection by enhancing risk-detection algorithms’ accuracy. Consequently, this lowers revenue loss and erroneous transaction declines.

In order to expedite loan distribution and user decision-making, an additional demonstration will demonstrate how an automated loan agent uses the Ntropy API to examine data on a bank’s website submit an appropriate investment report.

What Is A Contextual AI?

Contextual AI perceives and reacts to its surroundings. This implies that when it answers, it takes into account the user’s location, prior actions, and other crucial information. These systems are designed to provide customized and relevant responses.

Contextual AI Advances Retrieval-Augmented Generation for FSI

A California-based company with headquarters in Mountain View, provides a production-grade AI platform that is perfect for developing corporate AI applications in knowledge-intensive FSI use cases. Retriever-augmented generation powers this platform.

In order to provide significantly higher accuracy in context-dependent tasks, the Contextual AI platform combines the entire RAG pipeline extraction, retrieval, reranking, and generation into a single, optimized system that can be set up in a matter of minutes and further customized and tuned in response to user requirements.

HSBC intends to employ contextual AI to retrieve and synthesize pertinent market outlooks, financial news, and operational papers in order to enhance research findings and process recommendations. Contextual AI’s pre-built applications, which include financial analysis, policy-compliance report production, financial advising inquiry resolution, and more, are also being used by other financial institutions.

A user may inquire, “What’s our forecast for central bank rates by Q4 2025?” for instance. With references to certain parts of the source, the Contextual AI platform would provide a succinct explanation and a precise response based on real documents.

Contextual AI works with the open-source NVIDIA TensorRT-LLM library and NVIDIA Triton Inference Server to improve LLM inference performance.

NayaOne Provides Digital Sandbox for Financial Services Innovation

London-based NayaOne Offers a Digital Sandbox for Financial Services Innovation. Customers may safely test and certify AI applications using NayaOne‘s AI sandbox before they are commercially deployed. Financial institutions may develop synthetic data and access hundreds of fintechs on its platform.

Customers may utilize the digital sandbox to better assure top performance and effective integration by benchmarking apps for accuracy, fairness, transparency, and other compliance standards.

The need for AI-powered financial services solutions is growing, and our partnership with NVIDIA enables organizations to use generative AI’s potential in a safe, regulated setting. “Its’re building an ecosystem that will enable financial institutions to prototype more quickly and efficiently, resulting in genuine business transformation and expansion projects.”

Customers may explore and experiment with optimal AI models using NayaOne‘s AI Sandbox, which makes use of NVIDIA NIM microservices, and more quickly deploy them. When using NVIDIA accelerated computing, NayaOne can analyze massive datasets for its fraud detection models up to 10 times quicker and with up to 40% less infrastructure cost than when using extensive CPU-based algorithms.

Using the open-source NVIDIA RAPIDS data science and AI libraries, the digital sandbox speeds up money movement fraud detection and prevention. At the NVIDIA AI Pavilion at Money20/20, the company will display its digital sandbox.

Securiti’s AI Copilot Enhances Financial Planning

Securiti’s very adaptable Data+AI platform enables customers to create secure, end-to-end corporate AI systems, supporting a wide variety of generative AI applications such as safe enterprise AI copilots and LLM training and tuning.

The business is currently developing a financial planning assistant that is driven by NVIDIA NIM. In order to deliver context-aware answers to customers’ financial inquiries, the copilot chatbot accesses a variety of financial data while abiding by privacy and entitlement regulations.

The chatbot pulls information from a number of sources, including investing research materials, customer profiles and account balances, and earnings transcripts. Securiti’s technology preserves controls like access entitlements while securely ingesting and preparing information for usage with high-performance, NVIDIA-powered LLMs. Lastly, it offers consumers personalized replies via an easy-to-use user interface.

Securiti ensured the secure usage of data while optimizing the LLM’s performance using the Llama 3 70B-Instruct NIM microservice. The company will demonstrate generative AI at Money20/20. The NVIDIA AI Enterprise software platform offers Triton Inference Server and NIM microservices.

Read more on Govindhtech.com

[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...

[jcmarchi]

AI sector study: Record growth masks serious challenges

New Post has been published on https://thedigitalinsider.com/ai-sector-study-record-growth-masks-serious-challenges/

AI sector study: Record growth masks serious challenges

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A comprehensive AI sector study – conducted by the Department for Science, Innovation and Technology (DSIT) in collaboration with Perspective Economics, Ipsos, and glass.ai – provides a detailed overview of the industry’s current state and its future prospects.

In this article, we delve deeper into the key findings and implications—drawing on additional sources to enhance our understanding.

Thriving industry with significant growth

The study highlights the remarkable growth of the UK’s AI sector. With over 3,170 active AI companies, these firms have generated £10.6 billion in AI-related revenues and employed more than 50,000 people in AI-related roles. This significant contribution to GVA (Gross Value Added) underscores the sector’s transformative potential in driving the UK’s economic growth.

Mark Boost, CEO of Civo, said: “In a space that’s been dominated by US companies for too long, it’s promising to see the government now stepping up to help support the UK AI sector on the global stage.”

The study shows that AI activity is dispersed across various regions of the UK, with notable concentrations in London, the South East, and Scotland. This regional dispersion indicates a broad scope for the development of AI technology applications across different sectors and regions.

Investment and funding

Investment in the AI sector has been a key driver of growth. In 2022, £18.8 billion was secured in private investment since 2016, with investments made in 52 unique industry sectors compared to 35 sectors in 2016.

The government’s commitment to supporting AI is evident through significant investments. In 2022, the UK government unveiled a National AI Strategy and Action Plan—committing over £1.3 billion in support for the sector, complementing the £2.8 billion already invested.

However, as Boost cautions, “Major players like AWS are locking AI startups into their ecosystems with offerings like $500k cloud credits, ensuring that emerging companies start their journey reliant on their infrastructure. This not only hinders competition and promotes vendor lock-in but also risks stifling innovation across the broader UK AI ecosystem.”

Addressing bottlenecks

Despite the growth and investment, several bottlenecks must be addressed to fully harness the potential of AI:

Infrastructure: The UK’s digital technology infrastructure is less advanced than many other countries. This bottleneck includes inadequate data centre infrastructure and a dependent supply of powerful GPU computer chips. Boost emphasises this concern, stating “It would be dangerous for the government to ignore the immense compute power that AI relies on. We need to consider where this power is coming from and the impact it’s having on both the already over-concentrated cloud market and the environment.”

Commercial awareness: Many SMEs lack familiarity with digital technology. Almost a third (31%) of SMEs have yet to adopt the cloud, and nearly half (47%) do not currently use AI tools or applications.

Skills shortage: Two-fifths of businesses struggle to find staff with good digital skills, including traditional digital roles like data analytics or IT. There is a rising need for workers with new AI-specific skills, such as prompt engineering, that will require retraining and upskilling opportunities.

To address these bottlenecks, the government has implemented several initiatives:

Private sector investment: Microsoft has announced a £2.5 billion investment in AI skills, security, and data centre infrastructure, aiming to procure more than 20,000 of the most advanced GPUs by 2026.

Government support: The government has invested £1.5 billion in computing capacity and committed to building three new supercomputers by 2025. This support aims to enhance the UK’s infrastructure to stay competitive in the AI market.

Public sector integration: The UK Government Digital Service (GDS) is working to improve efficiency using predictive algorithms for future pension scheme behaviour. HMRC uses AI to help identify call centre priorities, demonstrating how AI solutions can address complex public sector challenges.

Future prospects and challenges

The future of the UK AI sector is both promising and challenging. While significant economic gains are predicted, including boosting GDP by £550 billion by 2035, delays in AI roll-out could cost the UK £150 billion over the same period. Ensuring a balanced approach between innovation and regulation will be crucial.

Boost emphasises the importance of data sovereignty and privacy: “Businesses have grown increasingly wary of how their data is collected, stored, and used by the likes of ChatGPT. The government has a real opportunity to enable the UK AI sector to offer viable alternatives.

“The forthcoming AI Action Plan will be another opportunity to identify how AI can drive economic growth and better support the UK tech sector.”

AI Safety Summit: The AI Safety Summit at Bletchley Park highlighted the need for responsible AI development. The “Bletchley Declaration on AI Safety” emphasises the importance of ensuring AI tools are transparent, fair, and free from bias to maintain public trust and realise AI’s benefits in public services.

Cybersecurity challenges: As AI systems handle sensitive or personal information, ensuring their security is paramount. This involves protecting against cyber threats, securing algorithms from manipulation, safeguarding data centres and hardware, and ensuring supply chain security.

The AI sector study underscores a thriving industry with significant growth potential. However, it also highlights several bottlenecks that must be addressed – infrastructure gaps, lack of commercial awareness, and skills shortages – to fully harness the sector’s potential.

(Photo by John Noonan)

See also: EU AI Act: Early prep could give businesses competitive edge

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, computing, cybersecurity, europe, government, infrastructure, investment, report, research, safety, security, strategy, study, supercomputer, uk

[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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To learn more about our products, services, and capabilities, please visit our website at http://www.lanshengic.com

[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