Intel launches AI GPUs for workstation system, works to gain ground on Nvidia

Intel Corporation has unveiled two new graphics processing units (GPUs) targeting the workstation market, set to compete with Nvidia in AI-powered computing. The tech giant estimates that entry-level workstation GPUs are roughly 30% of the overall workstation market, and it believes the size warrants the need to increase its market presence.

The new Arc Pro B50 and B60 series workstation GPUs start at $299 and $500, respectively, and cater to customers looking for on-premise AI processing that does not rely on cloud providers like Amazon, Google, or Microsoft.

Intel has been developing its Arc GPU architecture for several years, initially releasing B-series chips named Battlemage for gamers and an A-series tailored toward enterprise-level graphics tasks. 

Intel release new AI-powered GPUs

According to Intel, the newly announced B50 and B60 cards are designed for AI workloads due to their enhanced memory and improved AI-specific performance metrics.

The Arc Pro B50, the mainstream AI workstation chip , comes with 16GB of memory and delivers up to 170 trillion operations per second (TOPS). Intel reiterated that this GPU is for AI-assisted software engineering and other design-centric workflows. 

The more powerful Arc Pro B60 has 24GB of memory and can handle up to 197 TOPS for customers running highly demanding AI applications. Intel released benchmark comparisons that showed performance gains over its previous-generation A50 6GB GPU and Nvidia’s competing RTX A1000 8GB. 

Per the results, the new Arc Pro B50 delivers up to 3.4 times the performance in graphics workloads compared to the A50, along with improvements over the RTX A1000 across various tasks. Intel also reported similar performance advantages in multiple AI inference benchmarks

“We expect this to be very competitive and a very attractive solution, particularly for design and engineering,” Vivian Lien, Intel’s vice president and general manager of client graphics, told reporters in a press conference.

Nvidia to improve AI infrastructure with humanoid robots

Meanwhile, Nvidia continues to lead the market with its RTX Pro AI chips and workstation Nvidia’s DGX Spark and DGX Station desktop machines. The chip manufacturer describes DGX Spark as a “compact supercomputer” powered by the Grace Blackwell GB10 chip. 

The DGX Station, outfitted with the more advanced GB300 Grace Blackwell Ultra, provides up to 784 GB of memory, which dwarfs Intel’s current offerings.

At Computex Taipei on Monday, Nvidia made several announcements, including advancements in humanoid robotics and expanded access to its NVLink technology. 

NVLink helps businesses connect with semi-custom AI servers leveraging Nvidia’s GPU infrastructure. It was developed to enable high-speed data exchange between chips and is used in its GB200 system, which integrates two Blackwell GPUs with a Grace CPU.

“NV link fusion is so that you can build semi-custom AI infrastructure, not just semi-custom chips,” the CEO reckoned.

CEO Jensen Huang called the physical AI sector the “next trillion-dollar industry.” He asserted that the company is currently focused on software platforms designed to train and operate humanoid robots in industrial settings.

During President Donald Trump’s visit to Saudi Arabia last week, the company also made headlines, announcing plans to supply several hundred thousand AI processors over five years to Humain, an AI startup owned by the Saudi sovereign wealth fund. 

Nvidia also unveiled DGX Cloud Lepton, an AI platform featuring a compute marketplace that connects developers to tens of thousands of GPUs via a global network of cloud providers. 

During his keynote at Computex, Huang announced the opening of a new office in Taiwan and a partnership with Foxconn to build an AI supercomputer in the country. 

“We are delighted to partner with Foxconn and Taiwan to help build Taiwan’s AI infrastructure and to support TSMC and other leading companies to advance innovation in the age of AI and robotics,” Huang concluded. 

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