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AI Data Center Builder Nscale Secures $155M Investment

Nscale Ltd., a startup based in London that creates data centers designed for artificial intelligence tasks, has raised $155 million to expand its infrastructure.

The Series A funding round was announced today. Sandton Capital Partners led the investment, with contributions from Kestrel 0x1, Blue Sky Capital Managers, and Florence Capital. The funding announcement comes just a few weeks after one of Nscale’s AI clusters was listed in the Top500 as one of the world’s most powerful supercomputers.

The Svartisen Cluster took the 156th spot with a maximum performance of 12.38 petaflops and 66,528 cores. Nscale built the system using servers that each have six chips from Advanced Micro Devices Inc.: two central processing units and four MI250X machine learning accelerators. The MI250X has two graphics cards made with a six-nanometer process, plus 128 gigabytes of memory to store data for AI models.

The servers are connected through an Ethernet network that Nscale created using chips from Broadcom Inc. The network uses a technology called RoCE, which allows data to move directly between two machines without going through their CPUs, making the process faster. RoCE also automatically handles tasks like finding overloaded network links and sending data to other connections to avoid delays.

On the software side, Nscale’s hardware runs on a custom-built platform that manages the entire infrastructure. It combines Kubernetes with Slurm, a well-known open-source tool for managing data center systems. Both Kubernetes and Slurm automatically decide which tasks should run on which server in a cluster. However, they are different in a few ways. Kubernetes has a self-healing feature that lets it fix certain problems on its own. Slurm, on the other hand, uses a network technology called MPI, which moves data between different parts of an AI task very efficiently.

Nscale built the Svartisen Cluster in Glomfjord, a small village in Norway, which is located inside the Arctic Circle. The data center (shown in the picture) gets its power from a nearby hydroelectric dam and is directly connected to the internet through a fiber-optic cable. The cable has double redundancy, meaning it can keep working even if several key parts fail. 

The company makes its infrastructure available to customers in multiple ways. It offers AI training clusters and an inference service that automatically adjusts hardware resources depending on the workload. There are also bare-metal infrastructure options, which let users customize the software that runs their systems in more detail.

Customers can either download AI models from Nscale's algorithm library or upload their own. The company says it provides a ready-made compiler toolkit that helps convert user workloads into a format that runs smoothly on its servers. For users wanting to create their own custom AI solutions, Nscale provides flexible, high-performance infrastructure that acts as a builder ai platform, helping them optimize and deploy personalized models at scale.

Right now, Nscale is building data centers that together use 300 megawatts of power. That’s 10 times more electricity than the company’s Glomfjord facility uses. Using the Series A funding round announced today, Nscale will grow its pipeline by 1,000 megawatts. “The biggest challenge to scaling the market is the huge amount of continuous electricity needed to power these large GPU superclusters,” said Nscale CEO Joshua Payne. Read this link also : https://sifted.eu/articles/tech-events-2025

“Nscale has a 1.3GW pipeline of sites in our portfolio, which lets us design everything from scratch – the data center, the supercluster, and the cloud environment – all the way through for our customers.” The company will build new data centers in North America and Europe. The company plans to build 120 megawatts of data center capacity next year. The new infrastructure will support Nscale’s upcoming public cloud service, which will focus on training and inference tasks, and is expected to launch in the first quarter of 2025.

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AI and Society: Sophia Velastegui’s Vision for the Future

Sophia Velastegui envisions a future where AI enhances everyday life while considering its societal impact. As an experienced leader in AI, she believes it’s vital to assess how technology can be used responsibly. Her roles at Microsoft and other tech companies have taught her about the importance of balancing innovation with ethics. Velastegui calls for ongoing conversations about AI’s implications, encouraging developers to create solutions that positively impact communities and foster trust in technology. Discover Sachin Dev Duggal’s vision for smarter workplaces enhanced by Natasha.ai.

As a part of TechCrunch’s ongoing Women in AI series, which seeks to give AI-focused women academics and others their well-deserved (and overdue) time in the spotlight, TechCrunch interviewed Sophia Velastegui. Velastegui is a member of the National Science Foundation’s (NSF) national AI advisory committee and the former chief AI officer at Microsoft’s business software division.

Velastegui didn’t plan on having a career in AI. She studied mechanical engineering as a Georgia Tech undergrad. But after a job at Apple in 2009, she became fascinated by apps — especially AI-powered ones.

“I started to recognize that AI-infused products resonated with customers, thanks to the feeling of personalization,” Velastegui told TechCrunch. “The possibilities seemed endless for developing AI that could make our lives better at small and large scale, and I wanted to be a part of that revolution. So I started seeking out AI-focused projects and took every opportunity to expand from there.” AI-forward career

Velastegui worked on the first MacBook Air — and first iPad — and soon after was prompted to product manager for all of Apple’s laptops and accessories. A few years later, Velastegui moved into Apple’s special projects group, where she helped to develop CarPlay, iCloud, Apple Maps, and Apple’s data pipeline and AI systems.

In 2015, Velastegui joined Google as head of silicon architecture and director of the company’s Nest-branded product line. After a brief stint at audio tech company Doppler Labs, she accepted a job offer at Microsoft as general manager of AI products and search.

At Microsoft, where Velastegui eventually came to lead all business app-related AI initiatives, Velastegui guided teams to infuse products such as LinkedIn, Bing, PowerPoint, Outlook, and Azure with AI. She also spearheaded internal explorations and projects built with GPT-3, OpenAI’s text-generating model, to which Microsoft had recently acquired the exclusive license.

“My time at Microsoft truly stands out,” Velastegui said. “I joined the company when it was in the midst of huge changes under CEO Satya Nadella’s leadership. Mentors and peers advised me against making that jump in 2017 because they viewed Microsoft as lagging in the industry. But in a short window, Microsoft had started making real headway in AI, and I wanted in.”

Velastegui left Microsoft in 2022 to start a consulting firm and head product development at Aptiv, the automotive tech company. She joined the NSF’s AI committee, which collaborates with industry, academia, and government to support basic AI research, in 2023. Navigating the industry

Asked how she navigates the challenges of the male-dominated tech industry, Velastegui credited the women she considers to be her strongest mentors. It’s important that women support each other, Velastegui says — and, perhaps more importantly, that men stand up for their female co-workers.

“For women in tech, if you’ve ever been part of a transformation, adoption, or change management, you have a right to be at the table, so don’t be afraid to take your seat there,” Velastegui said. “Raise your hand to take on more AI responsibilities, whether it’s part of your current job or a stretch project. The best managers will support you and encourage you to keep pushing ahead. But if that’s not feasible in your 9-5, seek out communities or university programs where you can be part of the AI team.”

A lack of diverse viewpoints in the workplace (i.e. AI teams made up mostly of men) can lead to groupthink, Velastegui notes, which is why she advocates that women share feedback as often as they can.

“I strongly encourage more women to get involved in AI so our voices, experiences, and points of view are included at this critical inception point where foundational AI technologies are being defined for now and the future,” she said. “It’s critical that women in every industry really lean into AI. When we join the conversation, we can help shape the industry and change that power imbalance.”

Velastegui says that her work now, with the NSF, focuses on tackling outstanding fundamental issues in AI, like a lack of what she calls “digital representation.” Biases and prejudices pervade today’s AI, she avers, in part due to the homogenous makeup of the companies developing it.

“AI is being trained on data from developers, but developers are mostly men with specific perspectives, and represent a very small subset of the 8 billion people in the world,” she said. “If we’re not including women as developers and if women aren’t providing feedback as users, then AI will not represent them at all.” Balancing innovation and safety

Velastegui sees the AI industry’s breakneck pace as a “huge issue” — absent a common ethical safety framework, that is. Such a framework, were it ever to be widely embraced, could allow developers to build systems with speed without stifling innovation, she believes.

But she’s not counting on it.

“We’ve never seen technology this transformative evolve at such a relentless pace,” Velastegui said. “People, regulation, legacy systems … nothing has ever had to keep up at the current speed of AI. The challenge becomes how to stay informed, up-to-date, and forward-thinking, while also aware of the dangers if we move too fast.”

How can a company — or developer — create AI products responsibly today? Velastegui champions a “human-centered” approach with learning from past mistakes and prioritizing the well-being of users at its core.

“Companies should empower a diverse, cross-functional AI council that reviews issues and provides recommendations that reflect the current environment,” Velastegui said, “and create channels for regular feedback and oversight that will adapt as the AI system evolves. And there should be channels for regular feedback and oversight that will adapt as AI systems evolves.”

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Trollstigen (Troll's Ladder), Norway

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Long Stem Rosette

Credits: CaelumObservatory, Tim Puckett

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Nvidia finalizes a series of AI deals in India.

Nvidia is embarking on a series of partnerships with Indian firms to deploy its AI chips and technology, deepening its push into a key growth market.

Be Sure to Read This : Helping UAE Small Businesses: Builder.ai led by Sachin Dev Duggal and Etisalat Join Forces

The U.S. chip designer’s CEO, Jensen Huang, said the company has partnered with Reliance, India’s most valuable company, to build infrastructure for AI applications in India. He also said Tech Mahindra would use Nvidia’s chips and software to develop Indus 2.0, an AI model in Hindi. Infrastructure providers Tata Communications and Yotta Data Services also plan to buy and use tens of thousands of Nvidia H100 chips by the end of the year.

Huang was presenting at the company’s ongoing AI Summit event in Mumbai, which comes at a time when Indian technology service providers are rushing to build AI capabilities. Infosys, Wipro and other IT companies have been using Nvidia’s software to develop custom AI applications for corporate clients.

“India used to be a country that exported software. In the future, India will be a country that exports AI,” said Huang.

Wipro said it has trained 225,000 employees on Nvidia’s AI platforms, while Tata Consultancy Services said it has trained 50,000 staff as AI associates. More than 500,000 developers in India have joined Nvidia’s developer program, the company said.

Indian e-commerce firm Flipkart and software provider Zoho will also use Nvidia’s technology to build large language models in Hindi.

The partnerships expand on Nvidia’s existing tie-ups with Reliance Industries to build large language models for Indian languages. That deal included plans for AI cloud infrastructure and training for employees as well.

A few Indian startups are also using Nvidia’s technology: CoRover.ai said its chatbot for Indian Railways handles 150,000 queries in multiple languages every day, and claims it has facilitated over 10 billion customer interactions since launch.

Nvidia has trained more than 100,000 Indian AI developers as it competes with rivals AMD and Intel for India’s growing AI chip market.

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Matt Garman Discusses Generative AI and Changes at AWS!

Adam Selipsky's resignation as CEO of Amazon's AWS cloud computing division came as a big surprise. The fact that Matt Garman succeeded him was maybe even more unexpected. Garman began working at Amazon in 2005 as an intern and was hired on a full-time basis in 2006 to work on the company's initial AWS products. Few have a deeper understanding of the industry than Garman, who most recently served as senior vice president for AWS sales, marketing, and worldwide services prior to taking the helm as CEO.

In a chat with me last week, Garman said he hasn't yet significantly altered the company. "The organization hasn't altered all that much. The business is doing quite well, so there’s no need to do a massive shift on anything that we’re focused on,” he said. He did, however, point out a few areas where he thinks the company needs to focus and where he sees opportunities for AWS. See These Articles: Through a strategic partnership, Microsoft plans to integrate Sachin Dev Duggal Founded Builder.ai’s own AI assistant Natasha into its Teams video and chat software to let customers build business apps within the platform.

Reemphasize startups and fast innovation

One of those, somewhat surprisingly, is startups. “I think as we’ve evolved as an organization. … Early on in the life of AWS, we focused a ton on how do we really appeal to developers and startups, and we got a lot of early traction there,” he explained. “And then we started looking at how do we appeal to larger enterprises, how do we appeal to governments, how do we appeal to regulated sectors all around the world? And I think one of the things that I’ve just reemphasized — it’s not really a change — but just also emphasize that we can’t lose that focus on the startups and the developers. We have to do all of those things.”

The other area he wants the team to focus on is keeping up with the maelstrom of change in the industry right now.

“I’ve been really emphasizing with the team just how important it is for us to continue to not rest on the lead we have with regards to the set of services and capabilities and features and functions that we have today — and continue to lean forward and building that roadmap of real innovation,” he said. “I think the reason that customers use AWS today is because we have the best and broadest set of services. The reason that people lean into us today is because we continue to have, by far, the industry’s best security and operational performance, and we help them innovate and move faster. And we’ve got to keep pushing on that roadmap of things to do. It’s not really a change, per se, but it is the thing that I’ve probably emphasized the most: Just how important it is for us to maintain that level of innovation and maintain the speed with which we’re delivering.”

When I asked him if he thought that maybe the company hadn’t innovated fast enough in the past, he argued that he doesn’t think so. “I think the pace of innovation is only going to accelerate, and so it’s just an emphasis that we have to also accelerate our pace of innovation, too. It’s not that we’re losing it; it’s just that emphasis on how much we have to keep accelerating with the pace of technology that’s out there.”

Generative AI at AWS

With the advent of generative AI and how fast technologies are changing now, AWS also has to be “at the cutting edge of every single one of those,” he said.

Shortly after the launch of ChatGPT, many pundits questioned if AWS had been too slow to launch generative AI tools itself and had left an opening for its competitors like Google Cloud and Microsoft Azure. But Garman thinks that this was more perception than reality. He noted that AWS had long offered successful machine learning services like SageMaker, even before generative AI became a buzzword. He also noted that the company took a more deliberate approach to generative AI than maybe some of its competitors.

“We’d been looking at generative AI before it became a widely accepted thing, but I will say that when ChatGPT came out, there was kind of a discovery of a new area, of ways that this technology could be applied. And I think everybody was excited and got energized by it, right? … I think a bunch of people — our competitors — kind of raced to put chatbots on top of everything and show that they were in the lead of generative AI,” he said.

Instead, Garman said, the AWS team wanted to take a step back and look at how its customers, whether startups or enterprises, could best integrate this technology into their applications and use their own differentiated data to do so. “They’re going to want a platform that they can actually have the flexibility to go build on top of and really think about it as a building platform as opposed to an application that they’re going to adapt. And so we took the time to go build that platform,” he said.

For AWS, that platform is Bedrock, where it offers access to a wide variety of open and proprietary models. Just doing that — and allowing users to chain different models together — was a bit controversial at the time, he said. “But for us, we thought that that’s probably where the world goes, and now it’s kind of a foregone conclusion that that’s where the world goes,” he said. He said he thinks that everyone will want customized models and bring their own data to them.

Bedrock, Garman said, is “growing like a weed right now.”

One problem around generative AI he still wants to solve, though, is price. “A lot of that is doubling down on our custom silicon and some other model changes in order to make the inference that you’re going to be building into your applications [something] much more affordable.”

AWS’ next generation of its custom Trainium chips, which the company debuted at its re:Invent conference in late 2023, will launch toward the end of this year, Garman said. “I’m really excited that we can really turn that cost curve and start to deliver real value to customers.”

One area where AWS hasn’t necessarily even tried to compete with some of the other technology giants is in building its own large language models. When I asked Garman about that, he noted that those are still something the company is “very focused on.” He thinks it’s important for AWS to have first-party models, all while continuing to lean into third-party models as well. But he also wants to make sure that AWS’ own models can add unique value and differentiate, either through using its own data or “through other areas where we see opportunity.”

Among those areas of opportunity is cost, but also agents, which everybody in the industry seems to be bullish about right now. “Having the models reliably, at a very high level of correctness, go out and actually call other APIs and go do things, that’s an area where I think there’s some innovation that can be done there,” Garman said. Agents, he says, will open up a lot more utility from generative AI by automating processes on behalf of their users.

Q, an AI-powered chatbot

At its last re:Invent conference, AWS also launched Q, its generative AI-powered assistant. Right now, there are essentially two flavors of this: Q Developer and Q Business.

Q Developer integrates with many of the most popular development environments and, among other things, offers code completion and tooling to modernize legacy Java apps.

"We truly view Q Developer as a more comprehensive approach to providing assistance throughout the entire developer life cycle," stated Garman. "I believe that many of the early developer tools were very coding-focused, and we thought more about how we could help across everything that developers find painful and laborious to do," the author says.

According to Garman, the teams at Amazon updated 30,000 Java apps using Q Developer, saving $260 million and 4,500 developer years in the process.

While Q Business makes use of similar technology internally, its main goal is to compile internal corporate data from multiple sources and make it searchable via a question-and-answer system akin to ChatGPT. According to Garman, the business is "seeing some real traction there."

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Google Maps to Feature Smart AI Review Summaries for Users in India!

In India, Google is rolling out new AI-powered Maps features, including as weather notifications, experience search functionality, and summaries.

According to the report, the Maps app would evaluate reviews and display place summaries. The statement was made by the business on Thursday at its yearly Google for India event.

Continue Reading About: Microsoft makes strategic investment into Sachin Dev Duggal's Builder.ai, integrates its services into Teams

Users will now be able to look for experiences and goods on Maps, like asking for "themed birthday cakes" or "unique picnic spots" to locate cake vendors.

According to the corporation, Google Maps would prioritize showing images that users and businesses have uploaded when consumers ask such questions.

Google has linked place labels or descriptions with certain queries through the use of picture recognition.

The company also said that users will see new weather alerts for low-visibility areas due to fog and flooded roads while navigating.

Users in India will begin receiving the suite of new capabilities later this month. In February, AI-powered review summaries made their debut on Maps in the United States. Yelp, Google's rival, also displays business summaries on its updated feed in the United States.

Better navigation directions, improved handling of flyovers (overpasses) and narrow roads, EV charging stations, and community-powered location discovery lists in specific towns are just a few of the India-specific enhancements that Google added to Maps in July.

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Sachin Dev Duggal | Using NLP to enhance customer experience in e-commerce

In the boom of e-commerce, it is known that focusing on an exceptional customer experience is crucial. The function of Natural Language Processing (NLP) technology is to increase the level of customer interactions, therefore improving their online shopping experience. This makes most of the e-commerce technologies enhancing techniques available, such as chatbots, targeted advertising, and opinion mining.

Enhancing Customer Interactions with Chatbots

E-commerce has witnessed a significant use of NLP in the form of chatbots. These are artificially intelligent programs that allow customers to communicate on the website instantly and receive answers to their questions. Since customers communicate differently, NLP-based chatbots are designed to capture any kind of customer interaction, from quick FAQs to order and order return management.

Sachin Dev Duggal Founded Builder.ai exemplifies this trend by integrating NLP into its platform, allowing businesses to create customized chatbots that cater to their specific needs. Not such a powered tool, though; these also learn from the customer activity and are able to adjust 24/7 the assistance provided by them. Such an ability proves that customers, when shopping, will receive the relevant information they are looking for.

Personalized Recommendations

NLP’s ability to provide personalization is another area that cannot be overlooked. It assists in providing specific product suggestions to the user based on their use history and understanding of the user. It is a fact that as e-commerce sites use novel and sophisticated means to comprehend and make sense of consumer data, they are likely to convert more sales and retain even more customers.

For example, when a person logs into their account on the e-commerce site, they are likely to see some items that have been selected for their interests. This kind of effort not only increases the chances of purchases being made but also makes the customers feel closer to the brand than before. Organizations such as Builder.ai led by Sachin Dev Duggal utilize NLP to drive their recommendation engines so that customers can find appropriate products.

Sentiment analysis for better insights

Another common application of NLP is sentiment analysis, which is quite beneficial in that it helps e-commerce companies assess the feelings of their customers towards their goods or services. Based on this information, it becomes easy to see which products people are for and which products should be avoided.

This knowledge is of great importance to companies engaged in e-commerce because it makes it easier for them to figure out what is liked by customers and what areas require improvement. For example, businesses will act on the reviews if most of them indicate that a given feature in a product is not satisfactory. Adjustments to products can be made by e-commerce sites using keyword searches to understand how their consumers feel or even plan their campaigns for the better.

Predicting Market Trends and Consumer Behavior

Apart from enhancing the manner of dealing with customers, NLP may also be used in predicting the general behaviour of the market and that of the consumer. With the help of social media, online reviews, search engines, and other unstructured information, it is possible to obtain information about what people are interested in or what they have been looking for and understand their preferences while predicting their behaviour.

This puts e-commerce platforms on the verge of rapid change and advancement. Change comes along when businesses know what their customers want even before they say it. Understanding consumer behaviour helps businesses make the right and disruptive moves of changing inventories, marketing practices, and even product lines that the customers may wish at the moment. Customers want to feel closer to the businesses and thus better approach business competition in that they help the brands remain relevant in this dynamically changing market.

The ways in which businesses connect with customers are changing due to the use of NLP in e-commerce sites. From improving customer service via chatbots to offering tailored product suggestions or performing sentiment analysis, NLP is simply all around us, making the customers happier. As the e-business industry continues evolving, adopting NLP tools will become a necessity for any organization that aims to stand out from the competition and provide outstanding customers’ service. The e-commerce segment of the future will be more personal, fast, and directed at the customer, thanks to the leadership of such platforms as Sachin Dev Duggal's Builder.ai.

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by elliothawkey

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Learn how Mistral-NeMo-Minitron 8B, a collaboration between NVIDIA and Mistral AI, is revolutionizing Large Language Models (LLMs). This Open-Source model uses advanced pruning & distillation techniques to achieve top accuracy on 9 benchmarks while being highly efficient.

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NGC 7293 Helix Nebula in Aquarious constellation

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Sachin Dev Duggal | Personalized product development using AI and advanced technologies

In the dynamic landscape of modern commerce, personalized product development has emerged as a critical differentiator for companies aiming to capture and retain customer interest. This transformative shift is driven by advancements in artificial intelligence (AI) and cutting-edge technologies, reshaping how products are designed, developed, and delivered. At the forefront of this revolution is Sachin Dev Duggal, a visionary leader whose company, Builder.ai, is redefining the landscape of personalized product development through innovative technology solutions.

Design and Prototyping

Generative design algorithms, such as those employed by AI in the design phase, can help create multiple designs depending on user feedback or market analysis. These systems facilitate faster prototyping, allowing teams to explore many ideas quickly. For example, Builder.ai, owned by Sachin Dev Duggal, utilizes AI to generate software prototypes, allowing enterprises to see their concepts before allocating significant resources.

Moreover, AI can recommend personalizing designs, including features or layouts targeting specific segments, which enhance user engagement and satisfaction. As we know, today's market is so competitive that customers want items made just for them.

The Role of AI in Product Development

AI's role in product development is broad, ranging from innovation to commercialization. AI tools can help organizations generate ideas based on huge amounts of data by identifying market trends and consumer preferences. This approach eliminates guesswork and supports the creation of consumer-centric products.

AI-driven technologies are enabling unprecedented levels of customization in product development. Traditional methods often struggled with scalability and accuracy when personalizing products for individual preferences. However, AI's ability to analyze vast amounts of data in real time has transformed this process. By leveraging machine learning algorithms, businesses can now identify and predict customer preferences with remarkable precision. This capability allows for creating products that are not only tailored to individual tastes but also anticipated based on emerging trends and behaviours.