Intel Accelerates AI Everywhere with Launch of Powerful Next-Gen Products

erek

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Interesting, another AI first company and like Nvidia no longer being a graphics company Intel joins the ranks with no longer being a CPU company?

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“Highlights include:
  • The Intel Core Ultra mobile processor family, the first built on the Intel 4 process technology and the first to benefit from the company's largest architectural shift in 40 years, delivers Intel's most power-efficient client processor and ushers in the age of the AI PC.
  • The 5th Gen Intel Xeon processor family is built with AI acceleration in every core, bringing leaps in AI and overall performance and lowering total cost of ownership (TCO).
  • Intel CEO Pat Gelsinger showed for the first time an Intel Gaudi 3 AI accelerator, arriving on schedule next year.
Intel Core Ultra Powers AI PC and New Applications

Intel Core Ultra represents the company's largest architectural shift in 40 years and launches the AI PC generation with innovation on all fronts: CPU compute, graphics, power, battery life and profound new AI features. The AI PC represents the largest transformation of the PC experience in 20 years, since Intel Centrino untethered laptops to connect to Wi-Fi from anywhere.

Intel Core Ultra features Intel's first client on-chip AI accelerator—the neural processing unit, or NPU—to enable a new level of power-efficient AI acceleration with 2.5x better power efficiency than the previous generation. Its world-class GPU and leadership CPU are each also capable of speeding up AI solutions.

New Xeon Brings More Powerful AI to the Data Center, Cloud, Network and Edge
The 5th Gen Intel Xeon processor family, also introduced today, brings a significant leap in performance and efficiency: Compared with the previous generation of Xeon, these processors deliver 21% average performance gain for general compute performance and enable 36% higher average performance per watt across a range of customer workloads. Customers following a typical five-year refresh cycle and upgrading from even older generations can reduce their TCO by up to 77%8.

Xeon is the only mainstream data center processor with built-in AI acceleration, with the new 5th Gen Xeon delivering up to 42% higher inference and fine-tuning on models as large as 20 billion parameters. It's also the only CPU with a consistent and ever-improving set of MLPerf training and inference benchmark results.

Xeon's built-in AI accelerators, together with optimized software and enhanced telemetry capabilities, enable more manageable and efficient deployments of demanding network and edge workloads for communication service providers, content delivery networks and broad vertical markets, including retail, healthcare and manufacturing.

During today's event, IBM announced that 5th Gen Intel Xeon processors achieved up to 2.7x better query throughput on its watsonx.data platform compared to previous-generation Xeon processors during testing 10. Google Cloud, which will deploy 5th Gen Xeon next year, noted that Palo Alto Networks experienced a 2x performance boost in its threat detection deep learning models by using built-in acceleration in 4th Gen Xeon through Google Cloud. And indie game studio Gallium Studios turned to Numenta's AI platform running on Xeon processors to improve inference performance by 6.5x over a GPU-based cloud instance, saving cost and latency in its AI-based game, Proxi.

This kind of performance unlocks new possibilities for advanced AI - not only in the data center and cloud, but across the world's networks and edge applications.

As important, Intel is partnering with more than 100 software vendors to bring several hundred AI-boosted applications to the PC market—a wide array of highly creative, productive and fun applications that will change the PC experience. For consumer and commercial customers, this means a larger and more extensive set of AI-enhanced applications will run great on Intel Core Ultra, particularly compared to competing platforms. For example, content creators working in Adobe Premiere Pro will enjoy 40% better performance versus the competition.

Intel Core Ultra-based AI PCs are available now from select U.S. retailers for the holiday season. Over the next year, Intel Core Ultra will bring AI to more than 230 designs from laptop and PC makers worldwide. AI PCs will comprise 80% of the PC market by 20284 and will bring new tools to the way we work, learn and create.

AI Acceleration and Solutions Everywhere Developers Need It
Both Intel Core Ultra and 5th Gen Xeon will find their way into places you might not expect. Imagine a restaurant that guides your menu choices based on your budget and dietary needs; a manufacturing floor that catches quality and safety issues at the source; an ultrasound that sees what human eyes might miss; a power grid that manages electricity with careful precision.

These edge computing use cases represent the fastest-growing segment of computing—projected to surge to a $445 billion global market by the end of the decade—within which AI is the fastest-growing workload. In that market, edge and client devices are driving 1.4x more demand for inference than the data center.

In many cases, customers will employ a mix of AI solutions. Take Zoom, which runs AI workloads on Intel Core-based client systems and Intel Xeon based-cloud solutions within its all-in-one communications and collaboration platform to deliver best user experience and costs. Zoom uses AI to suppress the neighbor's barking dog and blur your cluttered home office, and to generate a meeting summary and email.

To make AI hardware technologies as accessible and easy-to-use as possible, Intel builds optimizations into the AI frameworks developers use (like PyTorch and TensorFlow) and offers foundational libraries (through oneAPI) to make software portable and highly performant across different types of hardware.

Advanced developer tools, including Intel's oneAPI and OpenVINO toolkit, help developers harness hardware acceleration for AI workloads and solutions and quickly build, optimize and deploy AI models across a wide variety of inference targets.

Sneak Peek: Intel Gaudi AI Accelerator
Wrapping up the event, Gelsinger provided an update on Intel Gaudi, coming next year. He showed for the first time the next-generation AI accelerator for deep learning and large-scale generative AI models. Intel has seen a rapid expansion of its Gaudi pipeline due to growing and proven performance advantages combined with highly competitive TCO and pricing. With increasing demand for generative AI solutions, Intel expects to capture a larger portion of the accelerator market in 2024 with its suite of AI accelerators led by Gaudi.

With partners and a broad ecosystem, Intel is unlocking new growth opportunities fueled by AI, bringing AI everywhere.”

https://www.techpowerup.com/316728/...ere-with-launch-of-powerful-next-gen-products
 
Funny enough in the AI, LLM performance metrics Intel is sitting comfortably between Nvidia and the Google/Facebook/Amazon custom silicon which is still way ahead of AMD.

I really don’t want AMD to play this game, they are going to because the shareholders are riding Lisa so hard her husbands getting jealous. But they are so very far down and AMD already struggles with too many half assed implementations that I just don’t want them to add one more half baked solution into the ring.

AMD suffers from ADD brain, they fixate on something, get it 70% done, then put it on a shelf labelled as Open Source, then forget about it for a year.
 
AI hardware mean right now having access to a lot of ram, lot of bandwidth between the ram and compute, depending of the strategy stuff like good AVX512 implementation or tensor to do stuff like:
rom 16-bit floating point with 32-bit accumulate to 8-bit and even 4-bit integer operations with 32-bit accumulate.

Some computer platform like apple M architecture went there naturally and can beat a 4090 by a good amount in some AI stuff and it is somewhat when not fully inline with other compute task (I imagine because AI was build to take advantage and run on what existed)
 
AI hardware mean right now having access to a lot of ram, lot of bandwidth between the ram and compute, depending of the strategy stuff like good AVX512 implementation or tensor to do stuff like:
rom 16-bit floating point with 32-bit accumulate to 8-bit and even 4-bit integer operations with 32-bit accumulate.

Some computer platform like apple M architecture went there naturally and can beat a 4090 by a good amount in some AI stuff and it is somewhat when not fully inline with other compute task (I imagine because AI was build to take advantage and run on what existed)
Apple doesn’t call their “AI”, AI, they are very clear they call it Machine Learning, and Apple’s Neural Engine is mature, well documented, and throughly battle tested.
 
Funny enough in the AI, LLM performance metrics Intel is sitting comfortably between Nvidia and the Google/Facebook/Amazon custom silicon which is still way ahead of AMD.
AMD as good AVX512 too (which is ironic... considering intel could have had the lead easily here), at least according to AMD but to other as well:
https://www.amd.com/content/dam/amd...ferencing-amd-epyc-processors-white-paper.pdf

And it could soon be hard to sell laptop without strong AI inference performance or provide the hardware of the next consoles if they go for a integrated SOC (could easily have them go for a third party solution)
 
Apple doesn’t call their “AI”, AI, they are very clear they call it Machine Learning, and Apple’s Neural Engine is mature, well documented, and throughly battle tested.
And the route they went with, mean that their compute had a lot of very high bandwith memory, a bit like having 32-64-128-192 gig of physical vram igpu
 
AMD as good AVX512 too (which is ironic... considering intel could have had the lead easily here), at least according to AMD but to other as well:
https://www.amd.com/content/dam/amd...ferencing-amd-epyc-processors-white-paper.pdf

And it could soon be hard to sell laptop without strong AI inference performance or provide the hardware of the next consoles if they go for a integrated SOC (could easily have them go for a third party solution)
The AI acceleration built into ARM for Android and Apple is good, mature, and well used. And users take notice when their new $0 with their cellular plan does video, audio, image enhancement, and that stuff better than their brand new $800 pc
 
Probably an inside job. Seems they always are.
I read somewhere this was a phishing attack against a *former* employee, whose credentials to the node.js repository were stolen and used to update a library the wallet used with malware. That doesn't say much good about the wallet provider.
 
I read somewhere this was a phishing attack against a *former* employee, whose credentials to the node.js repository were stolen and used to update a library the wallet used with malware. That doesn't say much good about the wallet provider.
Ah yes, the whole, one minor functionary who no longer works here had permissions and credentials that gave them unrestricted access to our most precious data and we just didn't have the means to detect it in time excuse...
I certainly haven't heard that one used before :rolleyes:
 
AMD suffers from ADD brain, they fixate on something, get it 70% done, then put it on a shelf labelled as Open Source, then forget about it for a year.

That is such a profound and true statement 🤣
 

Intel says it will miss its AI goals with Gaudi 3 due to unbaked software — Intel's $500 million AI goal unachievable as competitors rake in billions​

News
By Anton Shilov
published November 1, 2024
But Gaudi 3 will probably hit it next year.

"While the Gaudi3 benchmarks have been impressive, and we are pleased by our recent collaboration IBM to deploy Gaudi 3 as a service on IBM Cloud, the overall uptake of Gaudi has been slower than we anticipated, as adoption rates were impacted by the product transition from Gaudi 2 to Gaudi 3, and software ease of use," said Pat Gelsinger, chief executive of Intel, at the company's earnings call with analysts and investors. "As a result, we will not achieve our target of $500 million in revenue for Gaudi in 2024."

Intel says the new Gaudi 3 accelerator offers tangible performance advantages over Gaudi 2 and can even challenge Nvidia's H100 (at least when this GPU does not use sparsity) in some cases. It is just as important that Gaudi 3 is significantly cheaper than the H100. Earlier this year, Intel disclosed that a kit featuring eight Gaudi 3 chips on a baseboard would be priced at $125,000, roughly $15,625 per chip. In comparison, a single Nvidia H100 card is currently priced at $30,678, around two times higher.

However, despite all the advantages that Gaudi 3 has, it looks like Intel's software was not exactly ready for prime time, which slowed down hardware purchases. Now, Intel expects Gaudi 3 sales to ramp up in 2025.

https://www.tomshardware.com/tech-i...-unachievable-as-competitors-rake-in-billions
 

Intel says it will miss its AI goals with Gaudi 3 due to unbaked software — Intel's $500 million AI goal unachievable as competitors rake in billions​

Seems to be a problem for AMD as well. AMD and Intel better hurry because the AI bubble pops and nobody needs this hardware.
 
Seems to be a problem for AMD as well. AMD and Intel better hurry because the AI bubble pops and nobody needs this hardware.
It's not going to pop exactly it will change shape, every AI tool I have seen so far or at least that is catered to me is a massive data analytics tool, that is valuable, and those will take off.
That crap people post on X and Facebook is for fun and just helps them collect more data as people upload data and interact with the output teaching it what is liked and what isn't liked so it can better refine it's other decision making processes.
But much of that generative stuff we get flooded with is just a loss leader and means of gathering large amounts of input and feedback on existing algorithms, aka free beta testers.
That data is being used to train the systems that will be helping the likes of Disney, Pixar, and the other animation studios crank out content at a more professional level.
 
It's not going to pop exactly it will change shape, every AI tool I have seen so far or at least that is catered to me is a massive data analytics tool, that is valuable, and those will take off.
That crap people post on X and Facebook is for fun and just helps them collect more data as people upload data and interact with the output teaching it what is liked and what isn't liked so it can better refine it's other decision making processes.
But much of that generative stuff we get flooded with is just a loss leader and means of gathering large amounts of input and feedback on existing algorithms, aka free beta testers.
That data is being used to train the systems that will be helping the likes of Disney, Pixar, and the other animation studios crank out content at a more professional level.
The way I see it is that companies saw this AI thing and wondered how they could make easy money? AI needs training to work, and the more training the better the results. This is why there's so much push for Nvidia hardware because they want to be first to have the most well trained AI for someone to buy. I'm not saying this won't have any real benefits, but the amount of training being put into this and the few people who want this are going to catch up eventually. I don't know if Intel trying to venture into this will work out as who knows how long this AI boom will last?
 
The way I see it is that companies saw this AI thing and wondered how they could make easy money? AI needs training to work, and the more training the better the results. This is why there's so much push for Nvidia hardware because they want to be first to have the most well trained AI for someone to buy. I'm not saying this won't have any real benefits, but the amount of training being put into this and the few people who want this are going to catch up eventually. I don't know if Intel trying to venture into this will work out as who knows how long this AI boom will last?
Oh yeah for every 10 AI startups, only 1 or 2 are going to make it to year 3, the rest will fold and likely get bought up by the ones who did make it for their hardware or training data.
 
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