Latest news with #H200
Yahoo
6 days ago
- Business
- Yahoo
Sify announces Pay-Per-Use Colocation Pricing at all NVIDIA-certified AI-Ready Hyperscale Data Center Campuses across India
CHENNAI, India, May 20, 2025 (GLOBE NEWSWIRE) -- Sify Technologies Limited (NASDAQ: SIFY), India's leading Digital ICT solutions provider, announced the launch of its unique Pay-per-use model to support the burgeoning requirements for AI Cloud Services. This follows the recent expansion of Sify's portfolio of DGX-Ready Data Centers, certified for up to 130 KW/rack capacity under NVIDIA's DGX-Ready Data Center program – Sify's latest hyperscale data centers in Chennai and Noida have now been certified by NVIDIA, joining Sify's Navi Mumbai data center which was certified in 2024. Sify will offer this innovative, colocation pricing program at all three of these campus locations. The hourly pricing model is inclusive of hosting, power and infrastructure costs. By pricing its services on an hourly basis, Sify is removing the entry-cost barrier and fixed-cost infrastructure risk, enabling its GPU Cloud partners to set up and respond quickly to the growing AI market. Sify's converged ICT ecosystem will also offer an extensive array of services like Global connectivity, White Glove IT infrastructure and Managed Services. Initially, Sify will support the NVIDIA H100, H200, B200, GB200 NVL72, and GB300 NVL72 platforms, including liquid-cooled variants. Speaking on this market disruption, Sharad Agarwal, CEO, Sify Infinit Spaces Limited, the data center subsidiary of Sify Technologies Limited, said 'By investing ahead in state-of-the-art infrastructure, Sify already has the most extensive footprint of NVIDIA-certified data centers and a network connectivity with the lowest latency to hyperscale clouds. Now, by introducing colocation pricing on an hourly basis, we aim to make it much faster and easier to deploy these platforms in India to support on-demand applications. Sify's colocation partners can bring the latest NVIDIA GPUs to India, while Sify will manage all of the local infrastructure to support this dynamic and rapidly evolving market.' 'India is rapidly emerging as a pivotal player in the global AI race, with its deep talent pool, cost advantages, and rapidly advancing digital infrastructure. Sify's pay-per-use colocation model, built on NVIDIA-certified platforms, is a timely innovation that removes traditional barriers to AI adoption. By enabling global enterprises to tap into India's AI capabilities through scalable, high-performance infrastructure, Sify is well positioned to support domestic innovation and position India as a global hub for AI workloads and transformation,' quoted Rajiv Ranjan, Associate Research Director, IDC Asia Pacific. About Sify Technologies A multiple times award winner of the Golden Peacock from the Institute of Directors for Corporate Governance, Sify Technologies is India's most comprehensive ICT service & solution provider. With Cloud at the core of our solutions portfolio, Sify is focussed on the changing ICT requirements of the emerging Digital economy and the resultant demands from large, mid and small-sized businesses. Sify's infrastructure, comprising state-of-the-art Data Centers, the largest MPLS network, partnership with global technology majors and deep expertise in business transformation solutions modelled on the cloud, make it the first choice of start-ups, SMEs and even large Enterprises on the verge of a revamp. More than 10000 businesses across multiple verticals have taken advantage of our unassailable trinity of Data Centers, Networks and Digital services and conduct their business seamlessly from more than 1700 cities in India. Internationally, Sify has presence across North America, the United Kingdom and Singapore. Sify, Sify Technologies and are registered trademarks of Sify Technologies Limited. Forward Looking Statements This press release contains forward-looking statements within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. The forward-looking statements contained herein are subject to risks and uncertainties that could cause actual results to differ materially from those reflected in the forward-looking statements. Sify undertakes no duty to update any forward-looking statements. For a discussion of the risks associated with Sify's business, please see the discussion under the caption 'Risk Factors' in the company's Annual Report on Form 20-F/A for the year ended March 31, 2024, which has been filed with the United States Securities and Exchange Commission and is available by accessing the database maintained by the SEC at and Sify's other reports filed with the SEC. For further information, please contact: Sify Technologies LimitedPraveen KrishnaInvestor Relations & Public Relations+91 20:20 Media Nikhila Kesavan+91 Weber ShandwickLucia Domville+1-212 546-8260LDomville@
Yahoo
15-05-2025
- Business
- Yahoo
atNorth to Host 6G AI Sweden's National AI Cloud
State-of-the-art AI infrastructure to be housed at atNorth's SWE01 data center in Stockholm. STOCKHOLM, May 15, 2025 /PRNewswire/ -- atNorth, the leading Nordic colocation, high-performance computing, and artificial intelligence service provider, has announced its hosting of infrastructure that will support the development of a state of the art National AI Cloud in partnership with 6G AI Sweden. 6G AI Sweden has an agreement to acquire Nvidia's latest AI-powered chips, the H200 and Blackwell GB200, which will support the development of a state-of-the-art National AI Cloud. This strategic move will also further the business's mission to deliver cutting-edge AI innovation across various industries. Located at atNorth's SWE01 data center in Stockholm, the National AI Cloud will ensure data sovereignty under Swedish jurisdiction and will be fully compliant with GDPR. Moreover, atNorth's SWE01 site leverages renewable energy sources and heat reuse technology to minimize environmental impact, reflecting 6G AI Sweden's commitment to sustainability. "This agreement is an important milestone for establishing 6G AI Sweden as a leading provider of sovereign AI infrastructure in Sweden," said M. A. Zaman, Founder & Chairman of 6G AI Sweden AB. "By incorporating Nvidia's powerful AI technologies and hosting our infrastructure at atNorth's SWE01 data center we can develop Sweden's first National AI Cloud and empower businesses to embrace the future of AI in a responsible way". "We look forward to hosting Sweden's leading National AI Cloud at our SWE01 data center," said Anders Fryxell, Chief Sales Officer at atNorth. "This partnership reflects a shared ethos of driving innovation while minimizing environmental impact. Together, we are committed to supporting AI development in a sustainable and responsible way." This news follows the announcement of atNorth's latest heat reuse partnership with Finnish retails giant, Kesko Corporation and the launch of its 2024 Sustainability Report. The business has also recently announced the securing of land in the Municipality of Sollefteå in Långsele, Sweden, for a potential new mega site to complement its existing metro sites in the country, and to contribute to its land bank. About atNorth atNorth is a leading Nordic data center services company that offers cost-effective, scalable colocation and high-performance computing services trusted by industry-leading organizations. The business acquired leading High Performance Computing (HPC) provider, Gompute, in 2023 enabling a compelling full stack offering tailored to AI and other critical high performance workloads. With sustainability at its core, atNorth's data centers run on renewable energy resources and support circular economy principles. All atNorth sites leverage innovative design, power efficiency, and intelligent operations to provide long-term infrastructure and flexible colocation deployments. The tailor-made solutions enable businesses to calculate, simulate, train and visualize data workloads in an efficient, cost-optimized way. atNorth is headquartered in Reykjavik, Iceland and operates eight data centers in strategic locations across the Nordics, with a site to open in Ballerup, Denmark in 2025, as well as its tenth under construction in Kouvola, Finland and its eleventh site in Ølgod, Denmark. The business has also secured land for a future mega site in the Sollefteå Municipality in Sweden. For more information, visit or follow atNorth on LinkedIn or Facebook. Press Contact:Anders Fryxell, CSO atNorthE-mail: This information was brought to you by Cision The following files are available for download: Release 6G AI SwedenAB LI View original content: Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Geeky Gadgets
14-05-2025
- Geeky Gadgets
TPUs vs GPUs the AI Hardware Decision : Why Your Hardware Choice Matters More Than Ever
What if the key to unlocking faster, more efficient AI development wasn't just in the algorithms you write, but in the hardware you choose? For years, the debate between Google's Tensor Processing Units (TPUs) and NVIDIA's Graphics Processing Units (GPUs) has divided developers, researchers, and tech enthusiasts alike. Both are engineered for artificial intelligence, yet their architectures and capabilities diverge in ways that can make or break your AI project. With NVIDIA's GPUs dominating the market and Google's TPUs offering specialized performance for certain tasks, the choice isn't as straightforward as it seems. Understanding the nuances of these technologies is no longer optional—it's essential for anyone navigating the rapidly evolving AI landscape. In this guide, Trelis Research explore the core differences between TPUs and GPUs, from memory architecture to cost efficiency, and how these impact real-world AI workloads. You'll discover why NVIDIA's H100 and H200 GPUs are often favored for scalability and affordability, while Google's TPU V6E shines in specific low-latency scenarios. We'll also delve into critical factors like parallelization techniques, software optimization, and deployment flexibility, offering insights that could transform how you approach AI hardware decisions. By the end, you'll have a clearer picture of which technology aligns best with your goals—and why the debate between TPU and GPU is far from over. TPU vs GPU Comparison Key Hardware Differences The fundamental differences between TPUs and GPUs stem from their hardware architecture and memory capabilities. NVIDIA's H100 GPU features an impressive 80 GB of VRAM with high-bandwidth memory (HBM), while the H200 takes this further with 141 GB of VRAM and even faster memory speeds. In contrast, Google's TPU V6E is equipped with only 32 GB of VRAM, which can be a significant limitation for memory-intensive tasks. Another critical distinction lies in interconnect speeds. TPUs have slower interconnects, which can hinder their ability to efficiently manage large-scale, distributed workloads. NVIDIA GPUs, with their advanced architecture, are better suited for handling such tasks, offering greater flexibility and scalability for developers. Performance: Speed and Scalability Performance is a pivotal factor when comparing AI hardware, as it directly impacts the efficiency and scalability of workloads. TPUs and GPUs exhibit notable differences in concurrency handling, throughput, and cost efficiency: Time to First Token: TPUs excel at generating the first token quickly under low concurrency levels. However, as concurrency increases, their performance diminishes, making them less suitable for large-scale applications requiring high parallelism. TPUs excel at generating the first token quickly under low concurrency levels. However, as concurrency increases, their performance diminishes, making them less suitable for large-scale applications requiring high parallelism. Token Throughput: NVIDIA GPUs, particularly the H200, outperform TPUs in overall throughput. This makes them ideal for high-demand AI models that require consistent and large-scale processing capabilities. NVIDIA GPUs, particularly the H200, outperform TPUs in overall throughput. This makes them ideal for high-demand AI models that require consistent and large-scale processing capabilities. Cost per Token: NVIDIA GPUs are more cost-effective. The H200 offers the lowest cost per token, followed by the H100, while TPUs are comparatively more expensive for similar workloads. These performance metrics highlight the scalability and cost advantages of NVIDIA GPUs, particularly for developers managing complex AI models or large datasets. NVIDIA GPUs vs Google TPUs: Which is Best for Your AI Project? Watch this video on YouTube. Enhance your knowledge on AI development by exploring a selection of articles and guides on the subject. Parallelization: Maximizing Efficiency Parallelization techniques are essential for optimizing hardware performance, especially in AI workloads. Both TPUs and GPUs support pipeline and tensor parallelization, but their effectiveness varies significantly: Pipeline Parallelization: This technique divides model layers across multiple devices, reducing VRAM usage. However, it increases the time to first token, making it less suitable for latency-sensitive tasks where quick responses are critical. This technique divides model layers across multiple devices, reducing VRAM usage. However, it increases the time to first token, making it less suitable for latency-sensitive tasks where quick responses are critical. Tensor Parallelization: By splitting matrices within layers, tensor parallelization enhances performance but demands substantial VRAM, particularly for storing key-value (KV) caches. NVIDIA GPUs, with their larger VRAM capacities, handle this method more effectively than TPUs. The larger memory capacity of NVIDIA GPUs gives them a distinct advantage in handling parallelization techniques, allowing them to deliver better performance and efficiency for complex AI workloads. Cost Efficiency Cost is a decisive factor for many developers, and NVIDIA GPUs consistently outperform TPUs in terms of cost-efficiency. The H200 GPU offers the lowest cost per token, followed closely by the H100. While TPUs deliver strong compute performance, their higher operational costs make them less appealing for budget-conscious developers. For most AI workloads, NVIDIA GPUs strike a better balance between performance and affordability, making them the preferred choice for developers seeking cost-effective solutions without compromising on efficiency. Software Optimization The role of software optimization in hardware performance cannot be overstated. NVIDIA GPUs benefit from a robust ecosystem of open source libraries, such as VLM, which are specifically optimized for their architecture. These libraries enable better compute utilization and practical performance, allowing developers to maximize the potential of their hardware. In contrast, TPUs often face software limitations that restrict their ability to achieve peak performance. This lack of optimization reduces their effectiveness in real-world applications, further tilting the balance in favor of Nvidia GPUs for most AI development scenarios. Accessibility and Deployment Accessibility is another critical factor when choosing AI hardware. Nvidia GPUs are widely available across multiple platforms, including RunPod, AWS, and Azure, offering developers flexibility in deployment. This multi-cloud support ensures that Nvidia GPUs can be integrated into a variety of workflows and environments. On the other hand, TPUs are restricted to Google Cloud, with limited access to higher configurations like V6E-16 or V6E-32. This lack of multi-cloud compatibility makes TPUs less attractive for developers seeking scalable and versatile solutions, further limiting their appeal in competitive AI markets. Future Outlook The future of AI hardware is poised for significant advancements, and Google's upcoming TPU V7E is expected to address some of the limitations of the V6E. Improvements in VRAM capacity and interconnect speeds, coupled with enhanced software optimization, could make TPUs more competitive with NVIDIA GPUs. However, until these advancements materialize, NVIDIA's H100 and H200 GPUs remain the superior choice for most AI workloads. Their combination of high performance, cost-efficiency, and accessibility ensures they continue to lead the market, offering developers reliable and scalable solutions for their AI projects. Media Credit: Trelis Research Filed Under: AI, Guides Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
Yahoo
14-05-2025
- Business
- Yahoo
1 Super Stock Down 76% You'll Regret Not Buying on the Dip in 2025
DigitalOcean provides cloud services exclusively to small and mid-sized businesses (SMBs). The company is expanding into artificial intelligence (AI) services, and revenue from this part of its business surged by 160% in the recent quarter. DigitalOcean stock is down significantly from its 2021 high, but its valuation is starting to look extremely attractive. 10 stocks we like better than DigitalOcean › The cloud computing industry is dominated by giants like Amazon, Microsoft, and Alphabet, but those providers typically focus on the largest and highest-spending enterprises. Tailoring their cloud services to small and medium-sized businesses (SMBs) wouldn't be an economical strategy, because those customers wouldn't contribute enough revenue to move the needle. DigitalOcean (NYSE: DOCN), on the other hand, focuses exclusively on providing cloud services to SMB customers. Plus, it has a growing portfolio of artificial intelligence (AI) services that are helping even the smallest businesses adopt this revolutionary technology. DigitalOcean stock is down 76% from its record high, which was set during the tech frenzy in 2021. The stock was undeniably overvalued then, but it's starting to look very attractive, especially in light of the company's accelerating revenue growth and soaring profits. Here's why investors might regret not buying the dip. DigitalOcean provides a range of cloud services to more than 600,000 customers, from simple data storage and website hosting to complex software development tools. It differentiates itself from the larger cloud platforms by offering cheap and transparent pricing, highly personalized support, and simple deployment processes. These attributes are suited to SMBs, especially those without in-house technical expertise. DigitalOcean is now helping SMBs access the power of AI. It operates data center infrastructure fitted with graphics processing units (GPUs) from top suppliers like Nvidia and Advanced Micro Devices. In order to keep prices down, DigitalOcean doesn't use the latest GPU variants, but it does offer Nvidia's H200 and AMD's MI300X, which can deliver more than enough computing power for moderate AI workloads. Plus, DigitalOcean offers fractional capacity, meaning SMBs can access between one and eight GPUs at a time. This is ideal for small businesses that might want to deploy an AI chatbot on their website to handle customer service inquiries, for example. That kind of workload doesn't require thousands of Nvidia's latest Blackwell GPUs, which is what the bigger cloud platforms are focused on providing. In January this year, DigitalOcean also launched a new platform called GenAI which allows SMBs to create custom AI agents to serve customers, onboard new employees, and even generate business insights from internal data. These agents are built on the latest large language models (LLMs) from top developers like OpenAI, Anthropic, and Meta Platforms, which are among the most complex in the world. The GenAI platform is still in beta mode, but DigitalOcean says 5,000 customers have already used it to deploy over 8,000 AI agents so far. DigitalOcean generated $210.7 million in total revenue during the first quarter of 2025 (ended March 31), which was a 14% increase from the year-ago period. That growth rate accelerated for the second consecutive quarter, which is a sign that momentum is building, and AI is a key reason why. Although DigitalOcean doesn't disclose exactly how much revenue its AI services generate, the company said it grew by an eye-popping 160% year over year during Q1. But it gets better -- management says demand for GPU capacity continues to outstrip supply, so investors should expect rapid growth from the AI business for the foreseeable future. DigitalOcean's Q1 results were even more impressive when you consider that it slashed its total operating expenses by 6% to improve its bottom line. In other words, the company could be growing its revenue even faster by investing more aggressively in costs like marketing, which would attract more customers. But the strategy worked like a charm. DigitalOcean's net income (profit) soared by a whopping 171% to $38.2 million during the quarter, which translated to $0.39 in earnings per share (EPS). When DigitalOcean stock peaked in 2021, it was trading at a lofty price-to-sales (P/S) ratio of around 30, which was unsustainable. The 76% decline in the stock since then, combined with the company's consistent revenue growth, has pushed its P/S ratio down to just 3.7. That's actually a 34% discount to its three-year average of 5.6, which excludes the 2021 period. Now that DigitalOcean is consistently profitable, we can also measure its valuation using the price-to-earnings (P/E) ratio. Based on the company's trailing 12-month EPS of $1.11, its stock trades at a P/E ratio of 27.6, which is near its cheapest level since it went public four years ago. The Nasdaq-100 index trades at a P/E ratio of 29.3, so DigitalOcean is cheaper than a basket of the world's biggest technology stocks (which includes many of the top providers of cloud and AI services). DigitalOcean values its addressable market at $400 billion this year, so it hasn't even scratched the surface of its opportunity. Considering the rapid growth of its AI revenue, its soaring earnings, and its valuation, investors might regret not buying the stock today when they look back on this moment in a few years' time. Before you buy stock in DigitalOcean, consider this: The Motley Fool Stock Advisor analyst team just identified what they believe are the for investors to buy now… and DigitalOcean wasn't one of them. The 10 stocks that made the cut could produce monster returns in the coming years. Consider when Netflix made this list on December 17, 2004... if you invested $1,000 at the time of our recommendation, you'd have $598,613!* Or when Nvidia made this list on April 15, 2005... if you invested $1,000 at the time of our recommendation, you'd have $753,878!* Now, it's worth noting Stock Advisor's total average return is 922% — a market-crushing outperformance compared to 169% for the S&P 500. Don't miss out on the latest top 10 list, available when you join . See the 10 stocks » *Stock Advisor returns as of May 12, 2025 John Mackey, former CEO of Whole Foods Market, an Amazon subsidiary, is a member of The Motley Fool's board of directors. Suzanne Frey, an executive at Alphabet, is a member of The Motley Fool's board of directors. Randi Zuckerberg, a former director of market development and spokeswoman for Facebook and sister to Meta Platforms CEO Mark Zuckerberg, is a member of The Motley Fool's board of directors. Anthony Di Pizio has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Advanced Micro Devices, Alphabet, Amazon, DigitalOcean, Meta Platforms, Microsoft, and Nvidia. The Motley Fool recommends the following options: long January 2026 $395 calls on Microsoft and short January 2026 $405 calls on Microsoft. The Motley Fool has a disclosure policy. 1 Super Stock Down 76% You'll Regret Not Buying on the Dip in 2025 was originally published by The Motley Fool Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
New Straits Times
09-05-2025
- Business
- New Straits Times
US move to ease curbs on chip exports an opportunity for Malaysia
KUALA LUMPUR: US President Donald Trump's plan to rescind the Biden-era artificial intelligence (AI) chip export rule presents opportunities for Malaysia to expand its AI chip industry and booming data centre sector. It could also deepen the country's specialisation in high-tech services and manufacturing, in line with national strategies to move the semiconductor industry up the value chain. "It can potentially boost foreign direct investments (FDIs) and export-driven growth for Malaysia," UOB Kay Hian Wealth Advisors Sdn Bhd head of investment research, Mohd Sedek Jantan, told Bernama in response to a reported move by the United States to rescind curbs on chip exports. He said it could enhance Malaysia's access to high-performance AI chips such as Nvidia's H100 and H200 series, which are critical for training large language models and powering next-generation data infrastructure. "This relaxation could significantly benefit Malaysia's booming data centre ecosystem, which has FDIs from global technology giants like Amazon Web Services, Microsoft and Google since 2023, which are American companies," he added. Mohd Sedek said Malaysia's ability to attract such capital stems from its comparative advantage in data-hosting capabilities, such as offering relatively low-cost electricity, political stability and geographic centrality in ASEAN. Increased chip access would enable Malaysia to deepen its specialisation in high-tech services and manufacturing, strengthening its position in global value chains. "Furthermore, this policy shift could facilitate a broader transformation within Malaysia's semiconductor ecosystem. Under the National Semiconductor Strategy (NSS), the government has signalled its intent for the sector to move up the value chain towards higher margin, front-end activities such as integrated circuit (IC) design and wafer fabrication," he continued. Companies such as Intel and Infineon have already pledged significant capital of US$7 billion and RM8 billion, respectively, for their Malaysian operations, particularly in Penang and Kulim in Kedah. As a result, Trump's policy pivot could accelerate this transition by removing technological constraints, enabling Malaysian firms and institutions to engage more directly with advanced chipsets. "Access to superior technologies can help Malaysia leapfrog into more innovative segments of the chip value chain, enhancing its total factor productivity and export complexity," he said. However, he cautioned that increased compliance burdens might arise from Washington's intensified focus on curbing technology leakages to China. "Malaysia has previously been flagged in US policy circles as a potential transhipment hub for dual-use technologies, and the Trump administration is likely to amplify scrutiny on such routes. Even if the licensing regime formally lifts quantitative restrictions, it may simultaneously impose qualitative requirements such as end-user certifications, real-time monitoring of exports, and stricter enforcement under Malaysia's Strategic Trade Act (STA) 2010," he added. Mohd Sedek said these requirements could inflate operating costs, particularly for small and medium enterprises (SMEs) in the semiconductor sector, thereby reducing their profitability and global competitiveness. According to news reports, the Trump administration plans to rescind Biden-era AI chip curbs as part of a broader effort to revise semiconductor trade restrictions that have drawn strong opposition from major tech companies and foreign governments. The repeal, which is not yet final, seeks to refashion a policy launched under President Joe Biden that created three broad tiers of countries for regulating the export of chips from Nvidia and others. The Trump administration reportedly will not enforce the so-called AI diffusion rule when it takes effect on May 15.
