The Samsung Galaxy S24 FE was announced yesterday, and the company has just unboxed it. This unboxing was presented in the form of a promo video which surfaced on the company’s official YouTube channel.

The Galaxy S24 FE unboxing video is now live on YouTube

The unboxing video has a duration of around a minute and a half, and it’s embedded at the very end of the article. It not only shows you what sits in the box, but you get to see all the colors of the device. On top of that, some features are highlighted here too.

In addition to the phone itself, and some paperwork, a SIM ejection pin is included, and a charging cable. That’s a USB-C to USB-C charging cable if you were wondering. No, the charger is not included in the box.

The phone comes in Blue, Graphite, Gray, Mint, and Yellow colors, in case you missed the memo yesterday. This video will show you the device from all angles. That way you’ll see its flat sides, which is a change from last year’s model. The device is also larger this time around, as it has a larger display.

Samsung claims this phone offers “stunning low-light portraits”

Samsung says that the Galaxy S24 FE can provide “stunning low-light portraits” thanks to ProVisual Engine. The cameras are highlighted in the video in general, and the same goes for the SoC. This phone is fueled by the Exynos 2400e chip.

A 6.7-inch AMOLED display with a 120Hz refresh rate is used this time around, instead of a 6.4-inch panel. It’s also brighter now, as it has a peak brightness of 1,900 nits. Gorilla Glass Victus+ protects that display, by the way.

The device is also water and dust-resistant, and so on. If you’d like to know more about the Galaxy S24 FE, check out our original announcement. You can also pre-order the device as we speak, if you’re interested.

Intel’s fortunes have declined so rapidly over the past year that chip designer ARM made a “high level inquiry” about buying its crown jewel product unit, Bloomberg reported. However, Intel said the division wasn’t for sale and turned down the offer, according to an unnamed insider.

There are two main units inside Intel, the product group that sells PC, server and networking chips and a chip manufacturing foundry. ARM had no interest in Intel’s foundry division, according to Bloomberg‘s sources. ARM and Intel representatives declined to comment.

Intel’s fortunes have been on the wane for years, but the decline over the last 12 months has been especially dramatic. Following a net $1.6 billion loss in Q2 2024, the company announced that it was laying off 15,000 employees as part of a $10 billion cost reduction plan. Last week, the company also revealed plans to transform its ailing foundry business into an independent subsidiary. Intel lost half its market value last year and is now worth $102.3 billion.

ARM sells its processor designs to Qualcomm, Apple and other manufacturers (mostly for mobile phones) but doesn’t build any chips itself. Purchasing Intel’s product division would completely transform its business model, though that scenario seems highly improbable.

With Intel wounded at the moment, rivals have been circling. Qualcomm also expressed interest in taking over Intel recently, according to a report from last week. Any mergers related to ARM and Qualcomm would be regulatory nightmares, but the fact that the offers exist at all shows Intel’s vulnerability.

Intel has other avenues to boost investment. Apollo Global Management (the owner of Yahoo and Engadget) has offered to invest as much as $5 billion in the company, according to a recent Bloomberg report. Intel also plans to sell part of its stake in chip-maker Altera to private equity investors.

This article is part of a VB Special Issue called “Fit for Purpose: Tailoring AI Infrastructure.” Catch all the other stories here.

AI is no longer just a buzzword — it’s a business imperative. As enterprises across industries continue to adopt AI, the conversation around AI infrastructure has evolved dramatically. Once viewed as a necessary but costly investment, custom AI infrastructure is now seen as a strategic asset that can provide a critical competitive edge.

Mike Gualtieri, vice president and principal analyst at Forrester, emphasizes the strategic importance of AI infrastructure. “Enterprises must invest in an enterprise AI/ML platform from a vendor that at least keeps pace with, and ideally pushes the envelope of, enterprise AI technology,” Gualtieri said. “The technology must also serve a reimagined enterprise operating in a world of abundant intelligence.” This perspective underscores the shift from viewing AI as a peripheral experiment to recognizing it as a core component of future business strategy.

The infrastructure revolution

The AI revolution has been fueled by breakthroughs in AI models and applications, but those innovations have also created new challenges. Today’s AI workloads, especially around training and inference for large language models (LLMs), require unprecedented levels of computing power. This is where custom AI infrastructure comes into play.

>>Don’t miss our special issue: Fit for Purpose: Tailoring AI Infrastructure.<<

“AI infrastructure is not one-size-fits-all,” says Gualtieri. “There are three key workloads: data preparation, model training and inference.” Each of these tasks has different infrastructure requirements, and getting it wrong can be costly, according to Gualtieri. For example, while data preparation often relies on traditional computing resources, training massive AI models like GPT-4o or LLaMA 3.1 necessitates specialized chips such as Nvidia’s GPUs, Amazon’s Trainium or Google’s TPUs.

Nvidia, in particular, has taken the lead in AI infrastructure, thanks to its GPU dominance. “Nvidia’s success wasn’t planned, but it was well-earned,” Gualtieri explains. “They were in the right place at the right time, and once they saw the potential of GPUs for AI, they doubled down.” However, Gualtieri believes that competition is on the horizon, with companies like Intel and AMD looking to close the gap.

The cost of the cloud

Cloud computing has been a key enabler of AI, but as workloads scale, the costs associated with cloud services have become a point of concern for enterprises. According to Gualtieri, cloud services are ideal for “bursting workloads” — short-term, high-intensity tasks. However, for enterprises running AI models 24/7, the pay-as-you-go cloud model can become prohibitively expensive.

“Some enterprises are realizing they need a hybrid approach,” Gualtieri said. “They might use the cloud for certain tasks but invest in on-premises infrastructure for others. It’s about balancing flexibility and cost-efficiency.”

This sentiment was echoed by Ankur Mehrotra, general manager of Amazon SageMaker at AWS. In a recent interview, Mehrotra noted that AWS customers are increasingly looking for solutions that combine the flexibility of the cloud with the control and cost-efficiency of on-premise infrastructure. “What we’re hearing from our customers is that they want purpose-built capabilities for AI at scale,” Mehrotra explains. “Price performance is critical, and you can’t optimize for it with generic solutions.”

To meet these demands, AWS has been enhancing its SageMaker service, which offers managed AI infrastructure and integration with popular open-source tools like Kubernetes and PyTorch. “We want to give customers the best of both worlds,” says Mehrotra. “They get the flexibility and scalability of Kubernetes, but with the performance and resilience of our managed infrastructure.”

The role of open source

Open-source tools like PyTorch and TensorFlow have become foundational to AI development, and their role in building custom AI infrastructure cannot be overlooked. Mehrotra underscores the importance of supporting these frameworks while providing the underlying infrastructure needed to scale. “Open-source tools are table stakes,” he says. “But if you just give customers the framework without managing the infrastructure, it leads to a lot of undifferentiated heavy lifting.”

AWS’s strategy is to provide a customizable infrastructure that works seamlessly with open-source frameworks while minimizing the operational burden on customers. “We don’t want our customers spending time on managing infrastructure. We want them focused on building models,” says Mehrotra.

Gualtieri agrees, adding that while open-source frameworks are critical, they must be backed by robust infrastructure. “The open-source community has done amazing things for AI, but at the end of the day, you need hardware that can handle the scale and complexity of modern AI workloads,” he says.

The future of AI infrastructure

As enterprises continue to navigate the AI landscape, the demand for scalable, efficient and custom AI infrastructure will only grow. This is especially true as artificial general intelligence (AGI) — or agentic AI — becomes a reality. “AGI will fundamentally change the game,” Gualtieri said. “It’s not just about training models and making predictions anymore. Agentic AI will control entire processes, and that will require a lot more infrastructure.”

Mehrotra also sees the future of AI infrastructure evolving rapidly. “The pace of innovation in AI is staggering,” he says. “We’re seeing the emergence of industry-specific models, like BloombergGPT for financial services. As these niche models become more common, the need for custom infrastructure will grow.”

AWS, Nvidia and other major players are racing to meet this demand by offering more customizable solutions. But as Gualtieri points out, it’s not just about the technology. “It’s also about partnerships,” he says. “Enterprises can’t do this alone. They need to work closely with vendors to ensure their infrastructure is optimized for their specific needs.”

Custom AI infrastructure is no longer just a cost center — it’s a strategic investment that can provide a significant competitive edge. As enterprises scale their AI ambitions, they must carefully consider their infrastructure choices to ensure they are not only meeting today’s demands but also preparing for the future. Whether through cloud, on-premises, or hybrid solutions, the right infrastructure can make all the difference in turning AI from an experiment into a business driver