Intel reported second quarter earnings on Thursday, showing a return to profitability after two straight quarters of losses and issuing a stronger-than-expected forecast. the stock rose 7% in extended trading.
Here’s how Intel did versus Refinitiv consensus expectations for the quarter ended July 1:
Earnings per share: 13 cents, adjusted, versus a loss of 3 cents expected by Refinitiv.
Revenue: $12.9 billion, versus $12.13 billion expected by Refinitiv.
For the third quarter, Intel expects earnings of 20 cents per share, adjusted, on revenue of $13.4 billion at the midpoint, versus analyst expectations of 16 cents per share on $13.23 billion in sales.
Intel posted net income of $1.5 billion, or 35 cents per share, versus a net loss of $454 million, or a loss of 11 cents per share, in the same quarter last year.
Revenue fell 15% to $12.9 billion from $15.3 billion a year ago, marking the sixth consecutive quarter of declining sales.
Intel CEO Pat Gelsinger said on a call with analysts the company still sees “persistent weakness” in all segments of its business through year-end, and that server chip sales won’t recover until the fourth quarter. He also said that cloud companies were focusing more on securing graphics processors for artificial intelligence instead of Intel’s central processors.
David Zinsner, Intel’s finance chief, said in a statement that part of the reason the report was stronger than expected was because of the progress the company has made toward slashing $3 billion in costs this year. Earlier this year, Intel slashed its dividend and announced plans to save $10 billion per year by 2025, including through layoffs.
“We have now exited nine lines of business since Gelsinger rejoined the company, with a combined annual savings of more than $1.7 billion,” said Zinsner.
Revenue in Intel’s Client Computing group, which includes the company’s laptop and desktop processor shipments, fell 12% to $6.8 billion. The overall PC market has been slumping for over a year. Intel’s server chip division, which is reported as Data Center and AI, saw sales decline 15% to $4 billion plus Intel’s Network and Edge division, which sells networking products for telecommunications, recorded a 38% decline in revenue to $1.4 billion.moreover Mobileye, a publicly traded Intel subsidiary focusing on self-driving cars, saw sales slip 1% on an annual basis to $454 million and Intel Foundry Services, the business that makes chips for other companies, reported $232 million in revenue.
Intel’s gross margin was nearly 40% on an adjusted basis, topping the company’s previous forecast of 37.5%. Investors want to see gross margins expand even as the company invests heavily in manufacturing capability.
In the first quarter, the company posted its largest loss ever as the PC and server markets slumped and demand declined for its central processors. Intel’s results on Thursday beat the forecast that management gave for the second quarter at the time.
Intel management has said the turnaround will take time and that the company is aiming to match TSMC’s chip-manufacturing prowess by 2026, which would enable it to bid to make the most advanced mobile processors for other companies, a strategy the company calls “five nodes in four years.” Intel said on Thursday that it remained on track to hit those goals.
Nvidia has had an amazing run, but any emerging technology, such as AI, which is bottlenecked by a single company will have issues in growth. Consulting firm McKinsey has pegged the AI market to be worth $1 trillion by 2030, but also that it was in an experimental and in early phases of commercial deployment.
While Nvidia will likely retain its leadership in GPU hardware as applied to AI for the foreseeable future, it is likely that other hardware solutions for AI systems will also be successful as AI matures. While technologist may quibble on specifics, all major AI hardware today are based on GPU architectures, and as such I will use the terms and concepts of AI hardware and GPU architecture somewhat interchangeably.
One likely candidate for AI related growth may be AMD (AMD), which has had GPU products since acquiring ATI in 2006.However, unlike Nvidia, which had a clear vision for of general-purpose GPU products (GPGPU), historically, AMD had largely kept its focus on the traditional gaming applications. AMD has developed an AI architecture called XDNA, and an AI accelerator called Alveo and announced its MI300, an integrated chip with GPU acceleration for high-performance computing and machine learning. How AMD can and may evolve in the AI may be subject of a different article.
Another contender for success in the AI applications using GPU is Intel, who is the focus of this article. Intel has maintained a consistent, if low key focus on GPU hardware focused on AI applications over the last decade. Intel’s integrated HD Graphics is built into most modern processor ICs; however, these are insufficient compared to dedicated GPUs for high-end inferencing or machine learning tasks.
It has 2 primary GPU architectures in production release:
In 2019 Intel Corporation acquired Habana Labs, an Israel-based developer of programmable deep learning accelerators for the data center for approximately $2 billion. Habana Labs’ Gaudi AI product line from its inception focused on AI deep learning processor technologies, rather than as GPU that has been extended to AI applications. As a result, Gaudi microarchitecture was designed from the start for the acceleration of training and inferencing. In 2022 Intel announced Gaudi2 and Greco processors for AI deep learning applications, implemented in 7-nanometer (TSMC) technology and manufactured on Habana’s high-efficiency architecture. Habana Labs benchmarked Gaudi2’s training throughput performance for the ResNet-50 computer vision model and the BERT natural language processing model delivering twice the training throughput over the Nvidia high end A100-80GB GPU. So, Gaudi appears to give Intel a competitive chip for AI applications.
Concurrent with the Habana Labs’ Gaudi development, Intel has internally developed the Xe GPU family, as dedicated graphics card to address high-end inferencing or machine learning tasks as well as more traditional high-end gaming. Iris® Xe GPU family consists of a series of microarchitectures, ranging from integrated/low power (Xe-LP) to enthusiast/high performance gaming (Xe-HPG), data center/AI (Xe-HP) and high-performance computing (Xe-HPC). The architecture has been commercialized in Intel® Data Center GPU Flex Series (formerly codenamed Arctic Sound) and Intel® Arc GPU cards. There is some question on Xe GPU future and evolution. Intel has shown less commitment to the traditional GPU space compared to Gaudi. Nonetheless, it does demonstrate Intel ability to design and field complex GPU products as its business requires.
Intel has many other AI projects underway. The Sapphire Rapids chips implements AI specific acceleration blocks including technology called AMX (Advanced Matrix Extensions), which provides acceleration inside the CPU for efficient matrix multiplications used in on-chip inferencing and machine learning processing by speeding up data movement and compression. Intel has supporting technologies such as Optane, which while cancelled as a production line, is available for their needs of a high-performance non-volatile memory, one of the intrinsic components in any AI product.
Based on the above, Intel appears to have competitive hardware solutions, however if we look at Nvidia success in AI, it is a result of a much a software and systems focus as it is the GPGPU hardware itself. Can Intel compete on that front. Ignoring for the moment that Intel has a huge software engineer (approx. 15,000) resource, it also has- access to one of the leading success stories in perhaps the most competitive AI application – self driving cars.
Mobileye, who was acquired by Intel in 2017, has been an early adopter and leader, with over 20 years of experience in automotive automated driving and vision systems. As such, Mobileye has a deep resource of AI domain information that should be relevant to many applications. Mobileye has announced that it is working closely with Habana, as related divisions within Intel. While Intel is in the process of re-spinning out Mobileye as public company, Mobileye Global Inc. (MBLY), at present Intel still owns over 95% of shares, keeping it effectively an Intel division.
In looking at Intel, we have a company with the history, resources, and technology to compete with Nvidia and infrastructure. They have made significant investment and commitment to the emerging AI market, in times when they have exited other profitable businesses. It should also be understood that AI related product are a small percentage of overall Intel revenues (INTC revenue are more than twice NVDA, even if NVDA has 6x its market cap), and continues to keep its primary business focus on its processor and foundry business.
Hopefully for shareholders, Intel continues to push their AI technology and business efforts. Their current position is that this is strategic, but Intel is in a very fluid time and priorities may change based on business, finances, and of course the general interest and enthusiasm for AI. It is always worth noting that AI as a technical concept is mature, and appears to be cyclical, with interest in the technical community rising and falling in hype and interest once every decade or so. I remember working on AI applications, at the time labeled as expert systems in the 1980s. If we are currently at a high hype point, this may be temporary, based on near term success and disappointment in what AI does achieve. Of course, as always, “this time is different” and the building blocks of effective AI systems currently exist, where for previous iterations, it was more speculative.
Here’s how Intel did versus Refinitiv consensus expectations for the quarter ended July 1:
Earnings per share: 13 cents, adjusted, versus a loss of 3 cents expected by Refinitiv.
Revenue: $12.9 billion, versus $12.13 billion expected by Refinitiv.
For the third quarter, Intel expects earnings of 20 cents per share, adjusted, on revenue of $13.4 billion at the midpoint, versus analyst expectations of 16 cents per share on $13.23 billion in sales.
Intel posted net income of $1.5 billion, or 35 cents per share, versus a net loss of $454 million, or a loss of 11 cents per share, in the same quarter last year.
Revenue fell 15% to $12.9 billion from $15.3 billion a year ago, marking the sixth consecutive quarter of declining sales.
Intel CEO Pat Gelsinger said on a call with analysts the company still sees “persistent weakness” in all segments of its business through year-end, and that server chip sales won’t recover until the fourth quarter. He also said that cloud companies were focusing more on securing graphics processors for artificial intelligence instead of Intel’s central processors.
David Zinsner, Intel’s finance chief, said in a statement that part of the reason the report was stronger than expected was because of the progress the company has made toward slashing $3 billion in costs this year. Earlier this year, Intel slashed its dividend and announced plans to save $10 billion per year by 2025, including through layoffs.
“We have now exited nine lines of business since Gelsinger rejoined the company, with a combined annual savings of more than $1.7 billion,” said Zinsner.
Revenue in Intel’s Client Computing group, which includes the company’s laptop and desktop processor shipments, fell 12% to $6.8 billion. The overall PC market has been slumping for over a year. Intel’s server chip division, which is reported as Data Center and AI, saw sales decline 15% to $4 billion plus Intel’s Network and Edge division, which sells networking products for telecommunications, recorded a 38% decline in revenue to $1.4 billion.moreover Mobileye, a publicly traded Intel subsidiary focusing on self-driving cars, saw sales slip 1% on an annual basis to $454 million and Intel Foundry Services, the business that makes chips for other companies, reported $232 million in revenue.
Intel’s gross margin was nearly 40% on an adjusted basis, topping the company’s previous forecast of 37.5%. Investors want to see gross margins expand even as the company invests heavily in manufacturing capability.
In the first quarter, the company posted its largest loss ever as the PC and server markets slumped and demand declined for its central processors. Intel’s results on Thursday beat the forecast that management gave for the second quarter at the time.
Intel management has said the turnaround will take time and that the company is aiming to match TSMC’s chip-manufacturing prowess by 2026, which would enable it to bid to make the most advanced mobile processors for other companies, a strategy the company calls “five nodes in four years.” Intel said on Thursday that it remained on track to hit those goals.
Nvidia has had an amazing run, but any emerging technology, such as AI, which is bottlenecked by a single company will have issues in growth. Consulting firm McKinsey has pegged the AI market to be worth $1 trillion by 2030, but also that it was in an experimental and in early phases of commercial deployment.
While Nvidia will likely retain its leadership in GPU hardware as applied to AI for the foreseeable future, it is likely that other hardware solutions for AI systems will also be successful as AI matures. While technologist may quibble on specifics, all major AI hardware today are based on GPU architectures, and as such I will use the terms and concepts of AI hardware and GPU architecture somewhat interchangeably.
One likely candidate for AI related growth may be AMD (AMD), which has had GPU products since acquiring ATI in 2006.However, unlike Nvidia, which had a clear vision for of general-purpose GPU products (GPGPU), historically, AMD had largely kept its focus on the traditional gaming applications. AMD has developed an AI architecture called XDNA, and an AI accelerator called Alveo and announced its MI300, an integrated chip with GPU acceleration for high-performance computing and machine learning. How AMD can and may evolve in the AI may be subject of a different article.
Another contender for success in the AI applications using GPU is Intel, who is the focus of this article. Intel has maintained a consistent, if low key focus on GPU hardware focused on AI applications over the last decade. Intel’s integrated HD Graphics is built into most modern processor ICs; however, these are insufficient compared to dedicated GPUs for high-end inferencing or machine learning tasks.
It has 2 primary GPU architectures in production release:
In 2019 Intel Corporation acquired Habana Labs, an Israel-based developer of programmable deep learning accelerators for the data center for approximately $2 billion. Habana Labs’ Gaudi AI product line from its inception focused on AI deep learning processor technologies, rather than as GPU that has been extended to AI applications. As a result, Gaudi microarchitecture was designed from the start for the acceleration of training and inferencing. In 2022 Intel announced Gaudi2 and Greco processors for AI deep learning applications, implemented in 7-nanometer (TSMC) technology and manufactured on Habana’s high-efficiency architecture. Habana Labs benchmarked Gaudi2’s training throughput performance for the ResNet-50 computer vision model and the BERT natural language processing model delivering twice the training throughput over the Nvidia high end A100-80GB GPU. So, Gaudi appears to give Intel a competitive chip for AI applications.
Concurrent with the Habana Labs’ Gaudi development, Intel has internally developed the Xe GPU family, as dedicated graphics card to address high-end inferencing or machine learning tasks as well as more traditional high-end gaming. Iris® Xe GPU family consists of a series of microarchitectures, ranging from integrated/low power (Xe-LP) to enthusiast/high performance gaming (Xe-HPG), data center/AI (Xe-HP) and high-performance computing (Xe-HPC). The architecture has been commercialized in Intel® Data Center GPU Flex Series (formerly codenamed Arctic Sound) and Intel® Arc GPU cards. There is some question on Xe GPU future and evolution. Intel has shown less commitment to the traditional GPU space compared to Gaudi. Nonetheless, it does demonstrate Intel ability to design and field complex GPU products as its business requires.
Intel has many other AI projects underway. The Sapphire Rapids chips implements AI specific acceleration blocks including technology called AMX (Advanced Matrix Extensions), which provides acceleration inside the CPU for efficient matrix multiplications used in on-chip inferencing and machine learning processing by speeding up data movement and compression. Intel has supporting technologies such as Optane, which while cancelled as a production line, is available for their needs of a high-performance non-volatile memory, one of the intrinsic components in any AI product.
Based on the above, Intel appears to have competitive hardware solutions, however if we look at Nvidia success in AI, it is a result of a much a software and systems focus as it is the GPGPU hardware itself. Can Intel compete on that front. Ignoring for the moment that Intel has a huge software engineer (approx. 15,000) resource, it also has- access to one of the leading success stories in perhaps the most competitive AI application – self driving cars.
Mobileye, who was acquired by Intel in 2017, has been an early adopter and leader, with over 20 years of experience in automotive automated driving and vision systems. As such, Mobileye has a deep resource of AI domain information that should be relevant to many applications. Mobileye has announced that it is working closely with Habana, as related divisions within Intel. While Intel is in the process of re-spinning out Mobileye as public company, Mobileye Global Inc. (MBLY), at present Intel still owns over 95% of shares, keeping it effectively an Intel division.
In looking at Intel, we have a company with the history, resources, and technology to compete with Nvidia and infrastructure. They have made significant investment and commitment to the emerging AI market, in times when they have exited other profitable businesses. It should also be understood that AI related product are a small percentage of overall Intel revenues (INTC revenue are more than twice NVDA, even if NVDA has 6x its market cap), and continues to keep its primary business focus on its processor and foundry business.
Hopefully for shareholders, Intel continues to push their AI technology and business efforts. Their current position is that this is strategic, but Intel is in a very fluid time and priorities may change based on business, finances, and of course the general interest and enthusiasm for AI. It is always worth noting that AI as a technical concept is mature, and appears to be cyclical, with interest in the technical community rising and falling in hype and interest once every decade or so. I remember working on AI applications, at the time labeled as expert systems in the 1980s. If we are currently at a high hype point, this may be temporary, based on near term success and disappointment in what AI does achieve. Of course, as always, “this time is different” and the building blocks of effective AI systems currently exist, where for previous iterations, it was more speculative.
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